Pseudotime Sexual Branch

Gametocyte Development in Plasmodium berghei

---

1. Introduction and Aims

We have merged the two datasets together in GCSKO_merge.Rmd. We also subsetted out the pre-sexual-branch and the sexual cells (male and female) and stored them in a Seurat object called tenx.mutant.integrated.sex. Here, we will perform pseudotime analysis on the dataset and build modules of genes that show similar expression across this pseudotime.

2. Read in the data

Load/Install the Required Packages

[1] "patchwork is loaded correctly"
[1] "viridis is loaded correctly"
[1] "Seurat is loaded correctly"
[1] "cowplot is loaded correctly"
[1] "gridExtra is loaded correctly"
[1] "grid is loaded correctly"
[1] "Hmisc is loaded correctly"
[1] "reshape2 is loaded correctly"
[1] "dplyr is loaded correctly"

Loading required package: circlize
========================================
circlize version 0.4.12
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

[1] "circlize is loaded correctly"
[1] "monocle3 is loaded correctly"

Loading required package: destiny

Attaching package: ‘destiny’

The following object is masked from ‘package:SummarizedExperiment’:

    distance

The following object is masked from ‘package:GenomicRanges’:

    distance

The following object is masked from ‘package:IRanges’:

    distance

[1] "destiny is loaded correctly"

Extrafont stuff to plot Arial

library("extrafont")
## The first time
#font_import()
## for pdf()
loadfonts()
## and/or for postscript()
loadfonts(device = "postscript") 

load data

## create a list of our mutant gene IDs
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

Read in gene annotations

gene_annotations <- read.table("../data/Reference/GenesByTaxon_Summary.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)
dim(gene_annotations)

## convert _ to -
gene_annotations$Gene.ID <- gsub("_", "-", gene_annotations$Gene.ID)

3. Dimensionalty Reduction

We will now re-calculate the UMAP, PCA and diffusion map to visualise the data since the old visualisation used the variation in the whole dataset and so some of the variation in this set of cells was obscured.

ref: https://github.com/satijalab/seurat/issues/1883

A. Recalculate PCA

The PCA is used as the basis of other dimensionality reductions so we will now recalculate this to get to our final UMAP.

First, run PCA again

tenx.mutant.integrated.sex <- RunPCA(tenx.mutant.integrated.sex, npcs = 30, verbose = FALSE)

Then inspect the PCs

ElbowPlot(tenx.mutant.integrated.sex, ndims = 30, reduction = "pca")

Have a quick look at the output

DimPlot(tenx.mutant.integrated.sex, reduction = "pca", pt.size = 0.01, label = TRUE)

And determine what is male and female:

FeaturePlot(tenx.mutant.integrated.sex, features = c("PBANKA-1319500", "PBANKA-0416100"), blend = TRUE, combine = TRUE, coord.fixed = TRUE, reduction = "pca")

Calculate new clusters

## generate new clusters at mid resolution
## 2
tenx.mutant.integrated.sex <- FindNeighbors(tenx.mutant.integrated.sex, dims = 1:11, reduction = "pca")
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 1, random.seed = 42, algorithm = 2)

## Plot
pca_plot <- DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, pt.size = 0.5, dims = c(1,2), reduction = "DIM_PCA", group.by = "seurat_clusters") + coord_fixed() 
## view
HoverLocator(plot = pca_plot, information = FetchData(tenx.mutant.integrated.sex, vars = c("identity_combined", "seurat_clusters")))

umap_clusters <- DimPlot(tenx.mutant.integrated.sex, 
                         label = TRUE, 
                         repel = TRUE, 
                         pt.size = 0.5, 
                         dims = c(1,2), 
                         reduction = "DIM_PCA", 
                         label.box = TRUE) +
                  coord_fixed() +
                  ## remove axes etc.
                  theme_void() + 
                  ## change colour of points
                  scale_color_discrete_qualitative(palette = "Dark 3") +
                  ## change background colour of labels
                  scale_fill_discrete_qualitative(palette = "Dark 3") + 
                  guides(colour = guide_legend(override.aes = list(size=5))) + 
                  theme(legend.text=element_text(size=10))

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

umap_clusters

save

ggsave("../images_to_export/ALLCELLS_cluster_sex_branch_locations.png", plot = umap_clusters, device = "png", path = NULL, scale = 1, width = 10, height = 12, units = "cm", dpi = 300, limitsize = TRUE)

## make list of genotypes
list_of_genotypes <- unique(tenx.mutant.integrated.sex@meta.data$identity_combined)
## redorder for better plotting
list_of_genotypes <- list_of_genotypes[c(1, 3, 2, 4, 11, 7, 8, 12, 5, 10, 6, 9)]

## make a blank list
list_plots_sex_highlight <- vector(mode = "list", length = length(list_of_genotypes))

## for loop
for(i in seq_along(list_of_genotypes)){
    ## make a list of cells to highlight - but for wild-type, include all cells of that genotype, not just ones excluded for sex ratio
  if (grepl("wild-type", list_of_genotypes[i]) == FALSE) {
  list_of_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == list_of_genotypes[i] & tenx.mutant.integrated.sex@meta.data$exclude_for_sex_ratio == FALSE), ]) } else
  {list_of_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == list_of_genotypes[i]), ])}
  ## make a plot
  pca_plot <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = list_of_cells, dims = c(1,2), reduction = "DIM_PCA") + 
  coord_fixed() + 
  scale_color_manual(values=c("#E2E2E2", "#31206E")) + 
  theme_void() + 
  labs(title = paste(list_of_genotypes[i])) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 15, face = "bold.italic"), legend.position = "none")
  ## add to the list
  list_plots_sex_highlight[[i]] <- pca_plot
}

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

plot

## this function writes the next bit of code for you
## put it into the console and paste the response
#ploty <- c()
#for(i in seq_along(list_of_genotypes)){
#  ploty <- paste0(ploty, "list_plots_sex_highlight[[", i, "]]", " + ")
#}

## plot
composite_cell_locations <- plot_grid(list_plots_sex_highlight[[1]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 13, face = "bold"), legend.position = "none"), 
                                      list_plots_sex_highlight[[2]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 13, face = "bold"), legend.position = "none"), 
                                      list_plots_sex_highlight[[3]], 
                                      list_plots_sex_highlight[[4]], 
                                      list_plots_sex_highlight[[5]], 
                                      list_plots_sex_highlight[[6]], 
                                      list_plots_sex_highlight[[7]], 
                                      list_plots_sex_highlight[[8]], 
                                      list_plots_sex_highlight[[9]], 
                                      list_plots_sex_highlight[[10]], 
                                      list_plots_sex_highlight[[11]], 
                                      list_plots_sex_highlight[[12]], 
                                      nrow = 2)

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composite_cell_locations

save

ggsave("../images_to_export/ALLCELLS_mutant_cell_locations.png", plot = composite_cell_locations, device = "png", path = NULL, scale = 1, width = 30, height = 20, units = "cm", dpi = 300, limitsize = TRUE)

Pseudotime

plot_cells(monocle.object.sex, color_cells_by="post_integration_clusters", group_cells_by="partition", x = 1, y = 2)

`select_()` was deprecated in dplyr 0.7.0.
Please use `select()` instead.The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
Please use the `.add` argument instead.

Redefine the identities of the male and female cells

male

monocle.object_male <- choose_graph_segments(monocle.object.sex)

female

monocle.object_female <- choose_graph_segments(monocle.object.sex)

bipotential

monocle.object_bipot <- choose_graph_segments(monocle.object.sex)

check

df_freq <- data.frame(table(c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot))))
paste("number of cells in seurat object is", length(colnames(monocle.object.sex)), ". The number of cells selected here with an identitity is", dim(df_freq)[1])

[1] "number of cells in seurat object is 3012 . The number of cells selected here with an identitity is 3012"

df_freq <- df_freq[df_freq$Freq > 1, ]
df_freq

Inspect where these missing cells are:

# '%ni%' <- Negate('%in%')
# 
# not_assigned_cells <- colnames(monocle.object)[colnames(monocle.object) %ni% c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))]
# 
# DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = not_assigned_cells, dims = c(2,1), reduction = "DIM_UMAP") +
#   coord_fixed() + 
#   scale_color_manual(values=c("#000000", "#f54e1e"))

## raw numbers for each identity
table(monocle.object_male@colData$identity_combined)
table(monocle.object_female@colData$identity_combined)
table(monocle.object_bipot@colData$identity_combined)

## remove cells not sorted unbiasedly
table(monocle.object_male@colData[monocle.object_male@colData$exclude_for_sex_ratio == FALSE & !is.na(monocle.object_male@colData$exclude_for_sex_ratio), ]$identity_combined)
table(monocle.object_female@colData[monocle.object_female@colData$exclude_for_sex_ratio == FALSE & !is.na(monocle.object_female@colData$exclude_for_sex_ratio), ]$identity_combined)
table(monocle.object_bipot@colData[monocle.object_bipot@colData$exclude_for_sex_ratio == FALSE & !is.na(monocle.object_bipot@colData$exclude_for_sex_ratio), ]$identity_combined)

## correct for clusters
m_meta <- monocle.object_male@colData
m_meta <- m_meta[m_meta$exclude_for_sex_ratio == FALSE & !is.na(m_meta$exclude_for_sex_ratio), ]
m_meta <- m_meta[which(m_meta$seurat_clusters %in% c(5, 10, 4, 9, 12, 16)), ]
table(m_meta$identity_combined)

f_meta <- monocle.object_female@colData
f_meta <- f_meta[f_meta$exclude_for_sex_ratio == FALSE & !is.na(f_meta$exclude_for_sex_ratio), ]
f_meta <- f_meta[which(f_meta$seurat_clusters %in% c(14, 15, 13, 8, 11, 7, 0)), ]
table(f_meta$identity_combined)

b_meta <- monocle.object_bipot@colData
b_meta <- b_meta[b_meta$exclude_for_sex_ratio == FALSE & !is.na(b_meta$exclude_for_sex_ratio), ]
b_meta <- b_meta[which(b_meta$seurat_clusters %in% c(1, 2, 3, 6)), ]
table(b_meta$identity_combined)

## create annotation dataframe from these results:
df_monocle_sexes <- rbind(data.frame("cell_name" = colnames(monocle.object_male), "sex" = rep("Male", length(colnames(monocle.object_male)))),
                          data.frame("cell_name" = colnames(monocle.object_female), "sex" = rep("Female", length(colnames(monocle.object_female)))),
                          data.frame("cell_name" = colnames(monocle.object_bipot), "sex" = rep("Bipotential", length(colnames(monocle.object_bipot))))
                          )

dim(df_monocle_sexes)

## order like the metadata
df_monocle_sexes <- df_monocle_sexes[match(rownames(monocle.object.sex@colData), df_monocle_sexes$cell_name), ]

## add this back into the monocle object
monocle.object.sex@colData$Sexes_monocle <- df_monocle_sexes$sex

Pseudotime Calculation

add pt to monocle and seurat

## extract pt values
pt_values <- as.data.frame(pseudotime(monocle.object.sex, reduction_method = "UMAP"))

## add to monocle
monocle.object.sex@colData$pseudotime <- pt_values[ ,1]

## add to seurat
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, pt_values, "pt_values_branch_all")

## plot
##extract pt values
df_pt_id <- monocle.object.sex@colData[,c("pseudotime", "Sexes_monocle")]
## extract UMAP coords
df_umap_plot <- tenx.mutant.integrated.sex@reductions[["DIM_PCA"]]@cell.embeddings
df_umap_plot <- merge(df_umap_plot, df_pt_id, by=0, all=TRUE)

## add tree
##The tree for monocle is located here:
# monocle.object@principal_graph_aux[["UMAP"]]$dp_mst 
ica_space_df <- t(monocle.object.sex@principal_graph_aux[["UMAP"]]$dp_mst) %>%
      as.data.frame() %>%
      dplyr::select_(prin_graph_dim_1 = "DIMPCA_1", prin_graph_dim_2 = "DIMPCA_2") %>%
      dplyr::mutate(sample_name = rownames(.),
                    sample_state = rownames(.))

dp_mst <- monocle.object.sex@principal_graph[["UMAP"]]

edge_df <- dp_mst %>%
      igraph::as_data_frame() %>%
      dplyr::select_(source = "from", target = "to") %>%
      dplyr::left_join(ica_space_df %>%
                         dplyr::select_(
                           source="sample_name",
                           source_prin_graph_dim_1="prin_graph_dim_1",
                           source_prin_graph_dim_2="prin_graph_dim_2"),
                       by = "source") %>%
      dplyr::left_join(ica_space_df %>%
                         dplyr::select_(
                           target="sample_name",
                           target_prin_graph_dim_1="prin_graph_dim_1",
                           target_prin_graph_dim_2="prin_graph_dim_2"),
                       by = "target")

## make ggplot
umap_id_pt <- ggplot(df_umap_plot, aes(x = DIMPCA_1, y = DIMPCA_2)) + 
                     geom_point(aes(color = pseudotime), size = 0.1) +
                     scale_colour_viridis(option = "plasma") +
                     theme_void() +
                     coord_fixed() +
                     geom_segment(aes_string(x="source_prin_graph_dim_1",
                                     y="source_prin_graph_dim_2",
                                     xend="target_prin_graph_dim_1",
                                     yend="target_prin_graph_dim_2"),
                          data=edge_df) +
                     theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 13, face = "bold")) +
                     labs(title = "Pseudotime")

umap_id_pt

plot of cell identity:

## extract meta data
df_pca <-tenx.mutant.integrated.sex@meta.data
## make new col
df_pca$assignment <- "unassigned"

## now add other annotations
## correct for clusters
## the problem with this approach is that cells that are na for unbiased sorting are excluded
#df_pca$assignment[which(rownames(df_pca) %in% rownames(m_meta))] <- "male"
#df_pca$assignment[which(rownames(df_pca) %in% rownames(f_meta))] <- "female"
#df_pca$assignment[which(rownames(df_pca) %in% rownames(b_meta))] <- "bipoential"
## correct for clusters
m_meta_2 <- monocle.object_male@colData
m_meta_2 <- m_meta_2[which(m_meta_2$seurat_clusters %in% c(5, 10, 4, 9, 12, 16)), ]

f_meta_2 <- monocle.object_female@colData
f_meta_2 <- f_meta_2[which(f_meta_2$seurat_clusters %in% c(14, 15, 13, 8, 11, 7, 0)), ]

b_meta_2 <- monocle.object_bipot@colData
b_meta_2 <- b_meta_2[which(b_meta_2$seurat_clusters %in% c(1, 2, 3, 6)), ]

df_pca$assignment[which(rownames(df_pca) %in% rownames(m_meta_2))] <- "male"
df_pca$assignment[which(rownames(df_pca) %in% rownames(f_meta_2))] <- "female"
df_pca$assignment[which(rownames(df_pca) %in% rownames(b_meta_2))] <- "progenitor"

## add back to meta data
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, df_pca$assignment, col.name = "assignment")

## remove these objects to save space
rm(m_meta_2, f_meta_2, b_meta_2)

## make a custom pal
# 1 = blue - "#0052c5"
# 2 = red - "#a52b1e"
# 3 = green - "#016c00"
# 4 = yellow - "#ffe400"
pal_sex <- c("#016c00", "#a52b1e", "#ffe400", "#E7E8EA")

UMAP_identity <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, repel = TRUE, pt.size = 0.5, group.by = "assignment", dims = c(1,2), reduction = "DIM_PCA") +
  coord_fixed() + 
  scale_colour_manual(values=pal_sex) + 
  theme_void() + 
  #theme(legend.position = "none")
  ## add sex symbols
  annotate("text", x = 30, y = -50, label = male_symbol, size=7, color="#a52b1e") + 
  annotate("text", x = 50, y = -10, label = female_symbol, size=7, color="#016c00") +
  labs(title = '')

## print
UMAP_identity

plot pseudotime vs. genotype

## extract sexes
male_df <- as.data.frame(monocle.object.sex@colData[which(monocle.object.sex@colData$Sexes_monocle == "Male"), ])
female_df <- as.data.frame(monocle.object.sex@colData[which(monocle.object.sex@colData$Sexes_monocle == "Female"), ])

## then only include cells that are:
## 1. unbiasedly sorted
## 2. in the right cluster
female_df <- female_df[female_df$exclude_for_sex_ratio == FALSE | is.na(female_df$exclude_for_sex_ratio), ]
female_df <- female_df[which(female_df$seurat_clusters %in% c(14, 15, 13, 8, 11, 7, 0)), ]
male_df <- male_df[male_df$exclude_for_sex_ratio == FALSE | is.na(male_df$exclude_for_sex_ratio), ]
male_df <- male_df[which(male_df$seurat_clusters %in% c(5, 10, 4, 9, 12, 16)), ]

## define the dataframes
male_df$identity_combined <- factor(male_df$identity_combined, levels = rev(c("md1", "md2", "md3", "md4", "md5", "gd1", "fd1", "fd2", "fd3", "fd4",  "wild-type (10x)", "wild-type (Smart-seq2)")))
female_df$identity_combined <- factor(female_df$identity_combined, levels = rev(c("md1", "md2", "md3", "md4", "md5", "gd1", "fd1", "fd2", "fd3", "fd4", "wild-type (10x)", "wild-type (Smart-seq2)")))

library(ggridges)
## plot females 
p1 <- ggplot(female_df, aes(x = pseudotime, y =  identity_combined, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 0.9, rel_min_height = 0.03,  quantile_lines = TRUE, quantiles = 2, point_alpha = 0.5, alpha = 0.8) +
  theme_classic() +
  scale_y_discrete(drop=FALSE) +
  labs(title = 'Female', x = "Female Branch Pseudotime", y = "Genotype") +
  scale_fill_viridis_c(name = "pseudotime", option = "C", alpha = 1) +
  theme(legend.position = "none", 
        panel.grid.major.y = element_line(size = (0.2), colour="grey"),
        axis.title.x=element_blank())

## plot males
p2 <- ggplot(male_df, aes(x = pseudotime, y =  identity_combined, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 0.9, rel_min_height = 0.03,  quantile_lines = TRUE, quantiles = 2, point_alpha = 0.5, alpha = 0.8, na.rm = FALSE)  +
  theme_classic() +
  scale_y_discrete(drop=FALSE) +
  labs(title = 'Male', x = "Male Branch Pseudotime") +
  scale_fill_viridis_c(name = "pseudotime", option = "C", alpha = 1) +
  theme(legend.position = "none",
        panel.grid.major.y = element_line(size = (0.2), colour="grey"),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

plot_grid(p1, p2, nrow = 1, align = "h", axis = "b")

Picking joint bandwidth of 4.78
Picking joint bandwidth of 2.58

#library(egg)
#egg::ggarrange(p1, p2,  nrow = 1)

#grid.newpage()
#grid.draw(cbind(ggplotGrob(p1), ggplotGrob(p2), size = "last"))

combined_df <- rbind(male_df, female_df)

combined_ridge_plots <- ggplot(combined_df, aes(x = pseudotime, y =  identity_combined, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 0.9, rel_min_height = 0.03,  quantile_lines = TRUE, quantiles = 2, point_alpha = 0.5, alpha = 0.8) +
  theme_classic() +
  scale_y_discrete(drop=FALSE) +
  labs(title = '', x = "Pseudotime", y = "") +
  scale_fill_viridis_c(name = "pseudotime", option = "C", alpha = 1) +
  theme(legend.position = "none", 
        panel.grid.major.y = element_line(size = (0.2), colour="grey"),
        axis.title.x=element_blank()) + 
  facet_grid(cols = vars(Sexes_monocle))

combined_ridge_plots

library(gridExtra)

combined_df <- rbind(male_df, female_df)
x <- as.data.frame(as.matrix.data.frame(table(combined_df$Sexes_monocle, combined_df$identity_combined)))
colnames(x) <- colnames(as.matrix(table(combined_df$Sexes_monocle, combined_df$identity_combined)))
rownames(x) <- rownames(as.matrix(table(combined_df$Sexes_monocle, combined_df$identity_combined)))
## transform
x <- t(x)
## reverse order of rows to match plots
x<- x[seq(dim(x)[1],1),]
ss <- tableGrob(x, theme = ttheme_minimal(base_family = "Arial"), rows=NULL)
grid.arrange(ss)

plot_grid(combined_ridge_plots, ss, nrow = 1, rel_widths = c(3, 1))

Picking joint bandwidth of 4.78
Picking joint bandwidth of 2.58

## A
composite_cell_locations <- plot_grid((UMAP_identity + labs(title=" ") + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "plain"), legend.position = "none")),
                                      (umap_id_pt + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "plain"), legend.position = "none")), 
                                      list_plots_sex_highlight[[3]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[4]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[5]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[6]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[7]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[8]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[9]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[10]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[11]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")), 
                                      list_plots_sex_highlight[[12]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "italic")),
                                      (list_plots_sex_highlight[[1]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "plain"), plot.subtitle = element_text(hjust = 0.5, family="Arial", size = 12, face = "plain"), legend.position = "none") + labs(title="wild-type", subtitle="(10x)")), 
                                      (list_plots_sex_highlight[[2]] + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 12, face = "plain"), plot.subtitle = element_text(hjust = 0.5, family="Arial", size = 12, face = "plain"), legend.position = "none") + labs(title="wild-type", subtitle="(Smart-seq2)")), 
                                      nrow = 2,
                                      axis = c("b")
                                      )

B <- plot_grid(combined_ridge_plots, ss, nrow = 1, rel_widths = c(3, 1))

Picking joint bandwidth of 4.78
Picking joint bandwidth of 2.58

## labels as uppercase letters:
#c(toupper(letters)[2:17])
# labels as roman numerals:
# c(tolower(as.roman(c(2:17))

Figure_publication <- plot_grid(composite_cell_locations, 
                                B,
                                ## add empty plot to give spacing
                                (ggplot() + theme_void()),
                                labels = c('', ''), 
                                label_size = 12, 
                                ncol = 1, 
                                nrow=3, 
                                rel_heights = c(1, 1, 1), 
                                rel_widths = c(1, 1, 1))

#ggdraw() +
#  draw_plot(composite_cell_locations, 0, .5, 1, .5) +
#  draw_plot(combined_ridge_plots, 0, 0, .5, .5) +
#  draw_plot(ss, .5, 0, .5, .5)

Figure_publication

save

ggsave("../images_to_export/Figure_4.png", plot = Figure_publication, device = "png", path = NULL, scale = 1, width = 21, height = 29.7, units = "cm", dpi = 300, limitsize = TRUE)

md3 only analysis figure

plot pseudotime vs. genotype

## extract sexes
male_df <- as.data.frame(monocle.object.sex@colData[which(monocle.object.sex@colData$Sexes_monocle == "Male"), ])
female_df <- as.data.frame(monocle.object.sex@colData[which(monocle.object.sex@colData$Sexes_monocle == "Female"), ])

## then only include cells that are:
## 1. unbiasedly sorted
## 2. in the right cluster
female_df <- female_df[which(female_df$seurat_clusters %in% c(14, 15, 13, 8, 11, 7, 0)), ]
male_df <- male_df[which(male_df$seurat_clusters %in% c(5, 10, 4, 9, 12, 16)), ]

## define the dataframes
male_df$identity_combined <- factor(male_df$identity_combined, levels = rev(c("md1", "md2", "md3", "md4", "md5", "gd1", "fd1", "fd2", "fd3", "fd4",  "wild-type (10x)", "wild-type (Smart-seq2)")))
female_df$identity_combined <- factor(female_df$identity_combined, levels = rev(c("md1", "md2", "md3", "md4", "md5", "gd1", "fd1", "fd2", "fd3", "fd4", "wild-type (10x)", "wild-type (Smart-seq2)")))

library(ggridges)

combined_df <- rbind(male_df, female_df)

combined_ridge_plots <- ggplot(combined_df, aes(x = pseudotime, y =  identity_combined, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 0.9, rel_min_height = 0.03,  quantile_lines = TRUE, quantiles = 2, point_alpha = 0.5, alpha = 0.8) +
  theme_classic() +
  scale_y_discrete(drop=FALSE) +
  labs(title = '', x = "Pseudotime", y = "") +
  scale_fill_viridis_c(name = "pseudotime", option = "C", alpha = 1) +
  theme(legend.position = "none", 
        panel.grid.major.y = element_line(size = (0.2), colour="grey"),
        axis.title.x=element_blank()) + 
  facet_grid(cols = vars(Sexes_monocle))

combined_ridge_plots

The only genotypes this plot includes that the other didn’t is:

table(monocle.object.sex@colData$exclude_for_sex_ratio, monocle.object.sex@colData$identity_name_updated)

       
        fd1 fd2 fd3 fd4 gd1 md1 md2 md3 md4 md5 wild-type
  FALSE  37 119  41  69  98  78 144  99  36 136       230
  TRUE   54   0   0   0   0  59  65  55   0   0       198

Get the numbers for this plot too

table(combined_df$identity_combined, combined_df$Sexes_monocle)

                        
                         Female Male
  wild-type (Smart-seq2)    196  216
  wild-type (10x)           222  171
  fd4                        23   45
  fd3                        19   14
  fd2                        62   54
  fd1                        23   65
  gd1                         1   33
  md5                        24  110
  md4                        15   15
  md3                        87   44
  md2                       205    0
  md1                       127    0

combined_ridge_plots <- ggplot(combined_df[which(combined_df$identity_combined %in% c("wild-type (Smart-seq2)", "wild-type (10x)", "md3") & combined_df$Sexes_monocle == "Male"),], aes(x = pseudotime, y =  identity_combined, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 0.9, rel_min_height = 0.03,  quantile_lines = TRUE, quantiles = 2, point_alpha = 0.5, alpha = 0.8) +
  theme_classic() +
  scale_y_discrete(drop=TRUE) +
  labs(title = '', x = "Pseudotime", y = "") +
  scale_fill_viridis_c(name = "pseudotime", option = "C", alpha = 1) +
  theme(legend.position = "none", 
        panel.grid.major.y = element_line(size = (0.2), colour="grey"),
        axis.title.x=element_blank()) + 
  facet_grid(cols = vars(Sexes_monocle))

combined_ridge_plots

ggsave("../images_to_export/MD3_ridge.png", plot = combined_ridge_plots, device = "png", path = NULL, scale = 1, width = 10, height = 5, units = "cm", dpi = 300, limitsize = TRUE)

## for this plot, I will add an extra column to the dataframe called md3_plot
## this will include a class for:
## notmd3_male
## notmd3_female
## notmd3_progenitor
## notmd3_unassigned
## md3_enriched
## md3_not_enriched

## the work flow is excatly the same as the male ansd female plot above, except, we then need to add a new column for md3 cells

## extract meta data
df_pca <-tenx.mutant.integrated.sex@meta.data
## make new col
df_pca$assignment <- "notmd3_unassigned"

## now add other annotations
## correct for clusters
## the problem with this approach is that cells that are na for unbiased sorting are excluded
#df_pca$assignment[which(rownames(df_pca) %in% rownames(m_meta))] <- "male"
#df_pca$assignment[which(rownames(df_pca) %in% rownames(f_meta))] <- "female"
#df_pca$assignment[which(rownames(df_pca) %in% rownames(b_meta))] <- "bipoential"
## correct for clusters
m_meta_2 <- monocle.object_male@colData
m_meta_2 <- m_meta_2[which(m_meta_2$seurat_clusters %in% c(5, 10, 4, 9, 12, 16)), ]

f_meta_2 <- monocle.object_female@colData
f_meta_2 <- f_meta_2[which(f_meta_2$seurat_clusters %in% c(14, 15, 13, 8, 11, 7, 0)), ]

b_meta_2 <- monocle.object_bipot@colData
b_meta_2 <- b_meta_2[which(b_meta_2$seurat_clusters %in% c(1, 2, 3, 6)), ]

df_pca$assignment[which(rownames(df_pca) %in% rownames(m_meta_2))] <- "notmd3_male"
df_pca$assignment[which(rownames(df_pca) %in% rownames(f_meta_2))] <- "notmd3_female"
df_pca$assignment[which(rownames(df_pca) %in% rownames(b_meta_2))] <- "notmd3_progenitor"

## then add md3 cells
df_pca$assignment[which(df_pca$identity_combined == "md3" & df_pca$exclude_for_sex_ratio == TRUE)] <- "md3_enriched"
df_pca$assignment[which(df_pca$identity_combined == "md3" & df_pca$exclude_for_sex_ratio == FALSE)] <- "md3_not_enriched"

## extract pca coords
df_umap_plot <- tenx.mutant.integrated.sex@reductions[["DIM_PCA"]]@cell.embeddings
df_umap_plot <- merge(df_umap_plot, df_pca, by=0, all=TRUE)

## reorder the table to plot md3 last
row.order <- c("8", "3","4", "15", "2", "18", "11",
               "7", "1", "13",
               "10", "14", "12", "16", "9",
               "5","6", "17",
               "19", "20")
## reorder using new order
df_umap_plot <- df_umap_plot[c(which(df_umap_plot$assignment != "md3_enriched" & df_umap_plot$assignment != "md3_not_enriched"), which(df_umap_plot$assignment == "md3_enriched" | df_umap_plot$assignment == "md3_not_enriched")), ]

## add back to meta data
#tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, df_pca$assignment, col.name = "md3_plot")

## remove these objects to save space
rm(m_meta_2, f_meta_2, b_meta_2)

## make a custom pal
# 1 = blue - "#0052c5"
# 2 = red - "#a52b1e"
# 3 = green - "#016c00"
# 4 = yellow - "#ffe400"
# grey - unassigned - "#E7E8EA"
# purple - "#31206E" 
pal_sex <- c("#31206E", "#31206E", "#016c00", "#a52b1e", "#ffe400", "#E7E8EA")

UMAP_identity <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, repel = TRUE, pt.size = 0.5, group.by = "md3_plot", dims = c(1,2), reduction = "DIM_PCA") +
  coord_fixed() + 
  scale_colour_manual(values=pal_sex) + 
  scale_alpha_manual(values=c(1, 1, 0.2, 0.2, 0.2, 0.2)) +
  theme_void() + 
  #theme(legend.position = "none")
  ## add sex symbols
  annotate("text", x = 30, y = -50, label = male_symbol, size=7, color="#a52b1e") + 
  annotate("text", x = 50, y = -10, label = female_symbol, size=7, color="#016c00") +
  labs(title = '')


UMAP_identity <- ggplot(df_umap_plot, aes(x = DIMPCA_1, y = DIMPCA_2, colour = assignment, alpha = assignment, shape = assignment, size = assignment)) +
  geom_point() +
  coord_fixed() + 
  scale_colour_manual(values=pal_sex) + 
  scale_alpha_manual(values=c(0.6, 0.6, 0.2, 0.2, 0.2, 0.2)) +
  scale_shape_manual(values=c(17, 19, 20, 20, 20, 20)) +
  scale_size_manual(values=c(4, 4, 2, 2, 2, 2), guide = guide_legend(override.aes = list(size = 3, alpha = 1))) +
  theme_void() + 
  #theme(legend.position = "none")
  ## add sex symbols
  annotate("text", x = 30, y = -50, label = male_symbol, size=7, color="#a52b1e") + 
  annotate("text", x = 50, y = -10, label = female_symbol, size=7, color="#016c00") +
  labs(title = '')

## print
UMAP_identity

ggsave("../images_to_export/MD3_pca.png", plot = UMAP_identity, device = "png", path = NULL, scale = 1, width = 15, height = 15, units = "cm", dpi = 300, limitsize = TRUE)

Differential expression

table(tenx.mutant.integrated.sex@meta.data$identity_combined, tenx.mutant.integrated.sex$seurat_clusters)

                        
                           0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16
  fd1                      0   0   1   1  28  31   0   0  22   2   2   0   0   1   0   0   3
  fd2                      0   0   0   1  15  30   2   0   2   3   6   0   0   0   3  57   0
  fd3                      9   5   1   2   7   5   0   5   0   2   0   5   0   0   0   0   0
  fd4                     11   0   1   0  24  14   0   9   2   4   3   1   0   0   0   0   0
  gd1                      0  17   4   8   0   0  14   0   0   0   0   0  25   0   1   0  29
  md1                     66   4   2   3   0   0   0  42   4   0   0   4   0  11   0   0   1
  md2                    102   3   0   1   0   0   0  55   1   0   0   4   0  42   0   1   0
  md3                     19  10   5   6  28   2   2   6  50   2   2  11   1   1   0   0   9
  md4                      7   1   2   1   0   0   1   7   0   0   0   1  16   0   0   0   0
  md5                     15   0   0   0   1   0   2   1   4  58  45   4   2   0   0   0   4
  wild-type (10x)         80 325 341 266  42  42 169   6  31  31  20  36  34   1  65   3   2
  wild-type (Smart-seq2)  84   4   2   6 118  73   4  54  24   9  10  15   1  18   0   1   5

## make pal
#pal_de <- c(diverging_hcl(2, palette = "Berlin")[1],"#E2E2E2", diverging_hcl(2, palette = "Berlin")[2])

## make df of above table
df <- as.data.frame(table(tenx.mutant.integrated.sex@meta.data$identity_combined, tenx.mutant.integrated.sex$seurat_clusters))
## change names
names(df) <- c("Genotype", "Cluster", "Frequency")
## reorder factor levels
df$Genotype <- factor(df$Genotype,levels = rev(levels(df$Genotype)))

## add colour column for clusters actually compared
df$col <- "white"
df[df$Genotype == "md3" & df$Cluster == "4", ]$col <- "mutant"
df[df$Genotype == "md4" & df$Cluster == "12", ]$col <- "mutant"
df[df$Genotype == "md5" & df$Cluster == "10", ]$col <- "mutant"
df[df$Genotype == "gd1" & df$Cluster == "12", ]$col <- "mutant"
df[df$Genotype == "fd1" & df$Cluster == "8", ]$col <- "mutant"
df[df$Genotype == "fd2" & df$Cluster == c("8", "15"), ]$col <- "mutant"
df[df$Genotype == "fd3" & df$Cluster == "0", ]$col <- "mutant"
df[df$Genotype == "fd4" & df$Cluster == "0", ]$col <- "mutant"
df[(df$Genotype == "wild-type (10x)" | df$Genotype == "wild-type (Smart-seq2)") & (df$Cluster == "0" | df$Cluster == "4" | df$Cluster == "8" | df$Cluster == "10" | df$Cluster == "12" | df$Cluster == "15"), ]$col <- "wild-type"

de_table <- ggplot(df, aes(Cluster, Genotype)) +
    geom_tile(aes(fill = col), color = "black") + 
    geom_text(aes(label = round(Frequency, 1))) +
  coord_fixed() +
  theme_minimal() +
    scale_fill_manual(breaks = c("mutant", "wild-type"), values = c(diverging_hcl(2, palette = "Berlin")[2],"#ffffff", diverging_hcl(2, palette = "Berlin")[1])) +
  labs(fill="Used for DE analysis") + 
  theme(text = element_text(size = 15, color = "black"), legend.position="bottom")

de_table

ggsave("../images_to_export/DE_table.png", plot = de_table, device = "png", path = NULL, scale = 1, width = 20, height = 30, units = "cm", dpi = 300, limitsize = TRUE)

From this, we should compare differential expression as such:

md3 - 4 md4 - 12 md5 - 10 gd1 - 12 (used) or 16 (seems to be it’s own cluster)? fd1 - 8 fd2 - has it’s own cluster of 15 -> compare 15 to 8 fd3 - 0 - not many cells fd4 - 0 - not many cells * for fd3 and fd4 you could do other clusters and look at changes along dev.

Things to look for:

1. is the expression of the marker (in mutants that make it to the end of gametocytogenesis) in the 820 lines disturbed?
2. Are any of the other screen hits disturbed?
3. What is the enrichment of these genes in

md3

cells.4 <- subset(tenx.mutant.integrated.sex, idents = "4")
DefaultAssay(cells.4) <- "RNA"
Idents(cells.4) <- "identity_combined"
md3_markers <- FindMarkers(cells.4, ident.1 = "md3", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

md3_markers_annotated <- md3_markers
md3_markers_annotated <- md3_markers_annotated[md3_markers_annotated$p_val_adj < 0.05, ]
head(md3_markers_annotated, n = 15)

md3_markers_annotated <- merge(gene_annotations, md3_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(md3_markers_annotated)

cells_to_plot <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$identity_combined == c("wild-type (10x)", "wild-type (Smart-seq2)", "md3"), ])
FeaturePlot(tenx.mutant.integrated.sex, features = c("PBANKA-1030100", "PBANKA-1206900"), dims = c(1,2), reduction = "DIM_PCA", cells = cells_to_plot, split.by = "genotype_combined")

cells_to_plot <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$identity_combined == c("wild-type (10x)", "wild-type (Smart-seq2)", "md3"), ])
FeaturePlot(tenx.mutant.integrated.sex, features = c("PBANKA-1030100", "PBANKA-1206900"), dims = c(1,2), reduction = "DIM_PCA", cells = cells_to_plot, split.by = "genotype_combined")

Some issues with actin 2 and beta tubulin which may prevent a fully fertile gametocyte

md4

cells.12 <- subset(tenx.mutant.integrated.sex, idents = "12")
DefaultAssay(cells.12) <- "RNA"
Idents(cells.12) <- "identity_combined"
md4_markers <- FindMarkers(cells.12, ident.1 = "md4", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

md4_markers_annotated <- md4_markers
md4_markers_annotated <- md4_markers[md4_markers_annotated$p_val_adj < 0.05, ]
head(md4_markers_annotated, n = 15)

md4_markers_annotated <- merge(gene_annotations, md4_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(md4_markers_annotated)

VlnPlot(cells.12, features = c("PBANKA-1209800", "PBANKA-0211500", "PBANKA-1222900", "PBANKA-1312700"), group.by = "identity_combined", pt.size = 0, combine = FALSE, slot = "data", assay = "RNA")

[[1]]

[[2]]

[[3]]

[[4]]

md5

cells.10 <- subset(tenx.mutant.integrated.sex, idents = "10")
DefaultAssay(cells.10) <- "RNA"
Idents(cells.10) <- "identity_combined"
md5_markers <- FindMarkers(cells.10, ident.1 = "md5", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

md5_markers_annotated <- md5_markers
md5_markers_annotated <- md5_markers_annotated[md5_markers_annotated$p_val_adj < 0.05, ]
head(md5_markers_annotated, n = 15)

md5_markers_annotated <- merge(gene_annotations, md5_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(md5_markers_annotated)

gd1

cells.12 <- subset(tenx.mutant.integrated.sex, idents = "12")
DefaultAssay(cells.12) <- "RNA"
Idents(cells.12) <- "identity_combined"
gd1_markers <- FindMarkers(cells.12, ident.1 = "gd1", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

gd1_markers_annotated <- gd1_markers
gd1_markers_annotated <- gd1_markers_annotated[gd1_markers_annotated$p_val_adj < 0.05, ]
head(gd1_markers_annotated, n = 15)

gd1_markers_annotated <- merge(gene_annotations, gd1_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(gd1_markers_annotated)

fd1

cells.8 <- subset(tenx.mutant.integrated.sex, idents = "8")
DefaultAssay(cells.8) <- "RNA"
Idents(cells.8) <- "identity_combined"
fd1_markers <- FindMarkers(cells.8, ident.1 = "fd1", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

fd1_markers_annotated <-fd1_markers
fd1_markers_annotated <- fd1_markers_annotated[fd1_markers_annotated$p_val_adj < 0.05, ]
head(fd1_markers_annotated, n = 15)

fd1_markers_annotated <- merge(gene_annotations, fd1_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(fd1_markers_annotated)

fd2

cells.15.8 <- subset(tenx.mutant.integrated.sex, idents = c("15", "8"))
DefaultAssay(cells.15.8) <- "RNA"
Idents(cells.15.8) <- "identity_combined"
## for fd2, because it sprawls, you have to take out pt < 40 too
keep_cells <- rownames(cells.15.8@meta.data[cells.15.8@meta.data$pt_values_branch_all < 40, ])
cells.15.8 <- subset(cells.15.8, cells = keep_cells)
fd2_markers <- FindMarkers(cells.15.8, ident.1 = "fd2", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

fd2_markers_annotated <- fd2_markers
fd2_markers_annotated <- fd2_markers_annotated[fd2_markers_annotated$p_val_adj < 0.05, ]
head(fd2_markers_annotated, n = 15)

fd2_markers_annotated <- merge(gene_annotations, fd2_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(fd2_markers_annotated)

fd3

cells.0 <- subset(tenx.mutant.integrated.sex, idents = "0")
DefaultAssay(cells.0) <- "RNA"
Idents(cells.0) <- "identity_combined"
fd3_markers <- FindMarkers(cells.0, ident.1 = "fd3", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Done!

fd3_markers_annotated <- fd3_markers
fd3_markers_annotated <- fd3_markers_annotated[fd3_markers_annotated$p_val_adj < 0.05, ]
head(fd3_markers_annotated, n = 15)

fd3_markers_annotated <- merge(gene_annotations, fd3_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(fd3_markers_annotated)

fd4

cells.0 <- subset(tenx.mutant.integrated.sex, idents = "0")
DefaultAssay(cells.0) <- "RNA"
Idents(cells.0) <- "identity_combined"
fd4_markers <- FindMarkers(cells.0, ident.1 = "fd4", ident.2 = c("wild-type (10x)", "wild-type (Smart-seq2)"), test.use = "MAST", verbose = FALSE)

Assuming data assay in position 1, with name et is log-transformed.

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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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 Completed [========================================================================================>----]  95% with 0 failures
 Completed [========================================================================================>----]  96% with 0 failures
 Completed [=========================================================================================>---]  96% with 0 failures
 Completed [=========================================================================================>---]  97% with 0 failures
 Completed [==========================================================================================>--]  97% with 0 failures
 Completed [==========================================================================================>--]  98% with 0 failures
 Completed [===========================================================================================>-]  98% with 0 failures
 Completed [===========================================================================================>-]  99% with 0 failures
 Completed [============================================================================================>]  99% with 0 failures
 Completed [============================================================================================>] 100% with 0 failures
 Completed [=============================================================================================] 100% with 0 failures
                                                                                                                               

Done!

fd4_markers_annotated <- fd4_markers
fd4_markers_annotated <- fd4_markers_annotated[fd4_markers_annotated$p_val_adj < 0.05, ]
head(fd4_markers_annotated, n = 15)

fd4_markers_annotated <- merge(gene_annotations, fd4_markers_annotated, by.x = "Gene.ID", by.y = 0)
View(fd4_markers_annotated)

how many DE genes per condition?

dim(md3_markers_annotated)[1]

[1] 19

dim(md4_markers_annotated)[1]

[1] 258

dim(md5_markers_annotated)[1]

[1] 103

dim(gd1_markers_annotated)[1]

[1] 250

dim(fd1_markers_annotated)[1]

[1] 149

dim(fd2_markers_annotated)[1]

[1] 1214

dim(fd3_markers_annotated)[1]

[1] 48

dim(fd4_markers_annotated)[1]

[1] 34

with filters of 2 fold change up or down (e.g. 0.5 > logFC > 1)

dim(md3_markers_annotated[md3_markers_annotated$avg_logFC > log(2) | md3_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 15

dim(md4_markers_annotated[md4_markers_annotated$avg_logFC > log(2) | md4_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 230

dim(md5_markers_annotated[md5_markers_annotated$avg_logFC > log(2) | md5_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 85

dim(gd1_markers_annotated[gd1_markers_annotated$avg_logFC > log(2) | gd1_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 197

dim(fd1_markers_annotated[fd1_markers_annotated$avg_logFC > log(2) | fd1_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 104

dim(fd2_markers_annotated[fd2_markers_annotated$avg_logFC > log(2) | fd2_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 739

dim(fd3_markers_annotated[fd3_markers_annotated$avg_logFC > log(2) | fd3_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 38

dim(fd4_markers_annotated[fd4_markers_annotated$avg_logFC > log(2) | fd4_markers_annotated$avg_logFC < log(0.5), ])[1]

[1] 32

filter

md3_markers_annotated_filtered <- md3_markers_annotated[md3_markers_annotated$avg_logFC > log(2) | md3_markers_annotated$avg_logFC < log(0.5), ]
md4_markers_annotated_filtered <- md4_markers_annotated[md4_markers_annotated$avg_logFC > log(2) | md4_markers_annotated$avg_logFC < log(0.5), ]
md5_markers_annotated_filtered <- md5_markers_annotated[md5_markers_annotated$avg_logFC > log(2) | md5_markers_annotated$avg_logFC < log(0.5), ]
gd1_markers_annotated_filtered <- gd1_markers_annotated[gd1_markers_annotated$avg_logFC > log(2) | gd1_markers_annotated$avg_logFC < log(0.5), ]
fd1_markers_annotated_filtered <- fd1_markers_annotated[fd1_markers_annotated$avg_logFC > log(2) | fd1_markers_annotated$avg_logFC < log(0.5), ]
fd2_markers_annotated_filtered <- fd2_markers_annotated[fd2_markers_annotated$avg_logFC > log(2) | fd2_markers_annotated$avg_logFC < log(0.5), ]
fd3_markers_annotated_filtered <- fd3_markers_annotated[fd3_markers_annotated$avg_logFC > log(2) | fd3_markers_annotated$avg_logFC < log(0.5), ]
fd4_markers_annotated_filtered <- fd4_markers_annotated[fd4_markers_annotated$avg_logFC > log(2) | fd4_markers_annotated$avg_logFC < log(0.5), ]

## remove isoforms from annotation
gene_annotations_no_isoforms <- gene_annotations[ ,-which(names(gene_annotations) %in% c("source_id"))]
gene_annotations_no_isoforms <- gene_annotations_no_isoforms[!duplicated(gene_annotations_no_isoforms), ]
md3_markers <- merge(gene_annotations_no_isoforms, md3_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
md4_markers <- merge(gene_annotations_no_isoforms, md4_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
md5_markers <- merge(gene_annotations_no_isoforms, md5_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
gd1_markers <- merge(gene_annotations_no_isoforms, gd1_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
fd1_markers <- merge(gene_annotations_no_isoforms, fd1_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
fd2_markers <- merge(gene_annotations_no_isoforms, fd2_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
fd3_markers <- merge(gene_annotations_no_isoforms, fd3_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)
fd4_markers <- merge(gene_annotations_no_isoforms, fd4_markers, by.x = "Gene.ID", by.y = 0, all.y = TRUE, all.x = FALSE)

save results of DE into excel

## make list of results
list_of_de_results <- list(md3 = md3_markers, 
                           md4 = md4_markers, 
                           md5 = md5_markers, 
                           gd1 = gd1_markers, 
                           fd1 = fd1_markers, 
                           fd2 = fd2_markers, 
                           fd3 = fd3_markers, 
                           fd4 = fd4_markers
                           )

for (i in seq_along(list_of_de_results)){
  ## change the _ to -
  list_of_de_results[[i]]$Gene.ID <- gsub("-", "_", list_of_de_results[[i]]$Gene.ID)
  ## add proper column names
  names(list_of_de_results[[i]]) <- c("Gene_ID", "Product_description", "Gene_type", "Is_pseudogene?", "Gene_name", "Transcript.Product.Description", "p_value", "average_logFC", "percent_cells_detected_1", "percent_cells_detected_2", "p_value_adjusted")
  ## remove is_pseudo
  list_of_de_results[[i]] <- list_of_de_results[[i]][ ,-which(names(list_of_de_results[[i]]) %in% "Transcript.Product.Description")]
  ## reorder
  list_of_de_results[[i]] <- list_of_de_results[[i]][ ,c(1, 5, 2, 3, 4, 8, 9, 6, 10, 7)]
}

library(openxlsx)
write.xlsx(list_of_de_results, file = "../data_to_export/DE_analysis.xlsx")

check

dim(md3_markers)[1]

[1] 1616

dim(md4_markers)[1]

[1] 2868

dim(md5_markers)[1]

[1] 1719

dim(gd1_markers)[1]

[1] 2711

dim(fd1_markers)[1]

[1] 2586

dim(fd2_markers)[1]

[1] 3011

dim(fd3_markers)[1]

[1] 1802

dim(fd4_markers)[1]

[1] 1518

save results of DE into excel

## another way to do it
# library(xlsx)
# write.xlsx(md3_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="md3", row.names=FALSE)
# write.xlsx(md4_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="md4", append=TRUE, row.names=FALSE)
# write.xlsx(md5_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="md5", append=TRUE, row.names=FALSE)
# write.xlsx(gd1_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="gd1", append=TRUE, row.names=FALSE)
# write.xlsx(fd1_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="fd1", append=TRUE, row.names=FALSE)
# write.xlsx(fd2_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="fd2", append=TRUE, row.names=FALSE)
# write.xlsx(fd3_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="fd3", append=TRUE, row.names=FALSE)
# write.xlsx(fd4_markers_annotated, file="../data_to_export/DE_analysis.xlsx", sheetName="fd4", append=TRUE, row.names=FALSE)

basic venn

library(ggvenn)
## set up palette
## there are 8 DE genotypes 
qualitative_hcl(8, palette = "Pastel 1")

[1] "#FFC5D0" "#F2CDB2" "#D4D8A7" "#AFE0B9" "#99E2D8" "#ABDBF3" "#D5D0FC" "#F5C6EE"

## make lists 
a <- list(`md3` = md3_markers_annotated_filtered$Gene.ID,
          `md4` = md4_markers_annotated_filtered$Gene.ID,
          `md5` = md5_markers_annotated_filtered$Gene.ID,
          `gd1` = gd1_markers_annotated_filtered$Gene.ID)
## plot
venn_male_md3 <- ggvenn(a, 
       c("md3", "md4", "md5", "gd1"),
       fill_color = c(qualitative_hcl(8, palette = "Pastel 1")[1], qualitative_hcl(8, palette = "Pastel 1")[2], qualitative_hcl(8, palette = "Pastel 1")[3], qualitative_hcl(8, palette = "Pastel 1")[4]),
       stroke_size = 0.5, set_name_size = 4,show_percentage = FALSE,
       fill_alpha = 0.5
       ) 
venn_male_md3

save

library("colorspace")
## make lists 
a <- list(`md4` = md4_markers_annotated_filtered$Gene.ID,
          `md5` = md5_markers_annotated_filtered$Gene.ID,
          `gd1` = gd1_markers_annotated_filtered$Gene.ID)
## plot
venn_male <- ggvenn(a, 
       c("md4", "md5", "gd1"),
       fill_color = c(qualitative_hcl(8, palette = "Pastel 1")[2], qualitative_hcl(8, palette = "Pastel 1")[3], qualitative_hcl(8, palette = "Pastel 1")[4]),
       stroke_size = 0.5, 
       set_name_size = 4,
       show_percentage = FALSE,
       fill_alpha = 0.5
       )
venn_male

library("colorspace")
## make lists 
a <- list(`md4` = md4_markers_annotated_filtered$Gene.ID,
          `md5` = md5_markers_annotated_filtered$Gene.ID,
          `gd1` = gd1_markers_annotated_filtered$Gene.ID)
## plot
venn_male <- ggvenn(a, 
       c("md4", "md5", "gd1"),
       fill_color = c(qualitative_hcl(8, palette = "Pastel 1")[2], qualitative_hcl(8, palette = "Pastel 1")[3], qualitative_hcl(8, palette = "Pastel 1")[4]),
       stroke_size = 0.5, 
       set_name_size = 4,
       show_percentage = FALSE,
       fill_alpha = 0.5
       )
venn_male

save

ggsave("../images_to_export/venn_male.png", plot = venn_male, device = "png", path = NULL, scale = 1, width = 5, height = 5, units = "cm", dpi = 300, limitsize = TRUE)

library("colorspace")
## make lists 
a <- list(`fd1` = fd1_markers_annotated_filtered$Gene.ID,
          `fd2` = fd2_markers_annotated_filtered$Gene.ID,
          `fd3` = fd3_markers_annotated_filtered$Gene.ID,
          `fd4` = fd4_markers_annotated_filtered$Gene.ID)
## plot
venn_female <- ggvenn(a, 
       #c("fd1", "fd2", "fd3", "fd4"),
       fill_color = c(qualitative_hcl(8, palette = "Pastel 1")[5], qualitative_hcl(8, palette = "Pastel 1")[6], qualitative_hcl(8, palette = "Pastel 1")[7], qualitative_hcl(8, palette = "Pastel 1")[8]),
       stroke_size = 0.5, 
       set_name_size = 4,
       show_percentage = FALSE,
       fill_alpha = 0.5
       )
venn_female

save

ggsave("../images_to_export/venn_female.png", plot = venn_female, device = "png", path = NULL, scale = 1, width = 5, height = 5, units = "cm", dpi = 300, limitsize = TRUE)

DE comparison figures

## make list of genotypes
list_of_genotypes <- unique(tenx.mutant.integrated.sex@meta.data$identity_combined)
## redorder for better plotting and remove wt, md1, and md2
list_of_genotypes <- list_of_genotypes[c(11, 7, 8, 12, 5, 10, 6, 9)]
## make a list of clusters to highlight for each genotype
list_of_clusters <- list(c("4"), c("12"), c("10"), c("12"), c("8"), c("15", "8"), c("0"), c("0"))

## make a blank list for plots
list_plots_de_highlight <- vector(mode = "list", length = length(list_of_genotypes))

## make pal
pal_de <- c(diverging_hcl(2, palette = "Berlin")[2],"#E2E2E2", diverging_hcl(2, palette = "Berlin")[1])

## for loop
for(i in seq_along(list_of_genotypes)){
    ## make a list of cells to highlight - but for wild-type, include all cells of that genotype, not just ones excluded for sex ratio
  umap_tx = tenx.mutant.integrated.sex@reductions[["DIM_PCA"]]@cell.embeddings
  umap_tx <- merge(umap_tx, tenx.mutant.integrated.sex@meta.data[,c("seurat_clusters", "identity_combined")], by=0, all=TRUE)
  ## make a new blank column
  umap_tx$col <- "not-profiled"
  ## add colours for each highlighted class
  umap_tx[umap_tx$seurat_clusters == list_of_clusters[[i]] & umap_tx$identity_combined == c("wild-type (10x)","wild-type (Smart-seq2)"), ]$col <- "wild-type"
  umap_tx[umap_tx$seurat_clusters == list_of_clusters[[i]] & umap_tx$identity_combined == list_of_genotypes[i], ]$col <- "mutant"
  ## make a size column 
  #umap_tx[umap_tx$col == "not-profiled", ]$pt_size <- "small"
  #umap_tx[umap_tx$col == "wild-type" | umap_tx$col == "mutant", ]$pt_size <- "big"
  ## order cells so that profiled cells are plotted last
  umap_tx <- rbind(umap_tx[umap_tx$col == "not-profiled", ], umap_tx[umap_tx$col == "wild-type", ], umap_tx[umap_tx$col == "mutant", ])
  ## make a plot
  pca_plot <- ggplot(umap_tx, aes(x = DIMPCA_1, y = DIMPCA_2)) +
              geom_point(aes(color = col, size = col, alpha = col)) +
              scale_colour_manual(values=pal_de) +
              scale_size_manual(values=c(3, 1, 3)) +
              scale_alpha_manual(values=c(0.5, 1, 0.5)) +
              coord_fixed() +
              theme_void() + 
              labs(title = paste(list_of_genotypes[i])) + 
              theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 15, face = "bold.italic"), legend.position = "none")
  ## add to the list
  list_plots_de_highlight[[i]] <- pca_plot
}

## manually edit fd2 as we had a slightly different criteria here:

for(i in 6){
    ## make a list of cells to highlight - but for wild-type, include all cells of that genotype, not just ones excluded for sex ratio
  umap_tx = tenx.mutant.integrated.sex@reductions[["DIM_PCA"]]@cell.embeddings
  umap_tx <- merge(umap_tx, tenx.mutant.integrated.sex@meta.data[,c("seurat_clusters", "identity_combined", "pt_values_branch_all")], by=0, all=TRUE)
  ## make a new blank column
  umap_tx$col <- "not-profiled"
  ## add colours for each highlighted class
  umap_tx[umap_tx$seurat_clusters == list_of_clusters[[i]] & umap_tx$identity_combined == c("wild-type (10x)","wild-type (Smart-seq2)") & umap_tx$pt_values_branch_all < 40, ]$col <- "wild-type"
  umap_tx[umap_tx$seurat_clusters == list_of_clusters[[i]] & umap_tx$identity_combined == list_of_genotypes[i] & umap_tx$pt_values_branch_all < 40, ]$col <- "mutant"
  ## make a size column 
  #umap_tx[umap_tx$col == "not-profiled", ]$pt_size <- "small"
  #umap_tx[umap_tx$col == "wild-type" | umap_tx$col == "mutant", ]$pt_size <- "big"
  ## order cells so that profiled cells are plotted last
  umap_tx <- rbind(umap_tx[umap_tx$col == "not-profiled", ], umap_tx[umap_tx$col == "wild-type", ], umap_tx[umap_tx$col == "mutant", ])
  ## make a plot
  pca_plot <- ggplot(umap_tx, aes(x = DIMPCA_1, y = DIMPCA_2)) +
              geom_point(aes(color = col, size = col, alpha = col)) +
              scale_colour_manual(values=pal_de) +
              scale_size_manual(values=c(3, 1, 3)) +
              scale_alpha_manual(values=c(0.5, 1, 0.5)) +
              coord_fixed() +
              theme_void() + 
              labs(title = paste(list_of_genotypes[i])) + 
              theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 15, face = "bold.italic"), legend.position = "none")
  ## add to the list
  list_plots_de_highlight[[6]] <- pca_plot
  rm(umap_tx)
  rm(pca_plot)
}

plot

## this function writes the next bit of code for you
## put it into the console and paste the response
#ploty <- c()
#for(i in seq_along(list_of_genotypes)){
#  ploty <- paste0(ploty, "list_plots_sex_highlight[[", i, "]]", " + ")
#}

## plot
composite_cell_locations <- plot_grid(list_plots_de_highlight[[1]], 
                                      list_plots_de_highlight[[2]], 
                                      list_plots_de_highlight[[3]], 
                                      list_plots_de_highlight[[4]], 
                                      list_plots_de_highlight[[5]], 
                                      list_plots_de_highlight[[6]], 
                                      list_plots_de_highlight[[7]], 
                                      list_plots_de_highlight[[8]], 
                                      nrow = 2)

composite_cell_locations

save

ggsave("../images_to_export/ALLCELLS_de_cell_locations.png", plot = composite_cell_locations, device = "png", path = NULL, scale = 1, width = 20, height = 10, units = "cm", dpi = 300, limitsize = TRUE)

Assessing DE genes

1. Look at whether screen hits are in the DE lists

n.b. negative means gene expression is lower in the mutant

Ignore the name of the mutant column - this needs to be updated in the original input file

2. Look at genes that are common between the lists

## read in screen hits
library("readxl")
screen_hits <- read_excel("../data/Screen/Modules_Clusters_Phenotypes.xlsx")

## change ID to - 
screen_hits$`new gene ID` <- gsub("_", "-", screen_hits$`new gene ID`)

## this page was really helpful in making this function: https://stackoverflow.com/questions/23559371/get-the-list-of-items-in-venn-diagram 
## it essentially makes a big list called elements that shows all the overlaps dor all combos
# 
# ## female
# list_of_list_of_DE_genes_female <- list(
#                               fd1 = fd1_markers_annotated_filtered$Gene.ID,
#                               fd2 = fd2_markers_annotated_filtered$Gene.ID,
#                               fd3 = fd3_markers_annotated_filtered$Gene.ID,
#                               fd4 = fd4_markers_annotated_filtered$Gene.ID
#                               )
# 
# 
# # get all combination
# # Create a list of all the combinations
# combs <- 
#   unlist(lapply(1:length(list_of_list_of_DE_genes_female), 
#                 function(j) combn(names(list_of_list_of_DE_genes_female), j, simplify = FALSE)),
#          recursive = FALSE)
# names(combs) <- sapply(combs, function(i) paste0(i, collapse = ""))
# str(combs)
# 
# library(prob)
# elements_female <- lapply(combs, function(i) setdiff(list_of_list_of_DE_genes_female[i], list_of_list_of_DE_genes_female[setdiff(names(list_of_list_of_DE_genes_female), i)]))
# 
# list_of_list_of_DE_genes_male <- list(md3 = md3_markers_annotated_filtered$Gene.ID,
#                               md4 = md4_markers_annotated_filtered$Gene.ID,
#                               md5 = md5_markers_annotated_filtered$Gene.ID,
#                               gd1 = gd1_markers_annotated_filtered$Gene.ID
#                               )
# 
# 
# # get all combination
# # Create a list of all the combinations
# combs <- 
#   unlist(lapply(1:length(list_of_list_of_DE_genes_male), 
#                 function(j) combn(names(list_of_list_of_DE_genes_male), j, simplify = FALSE)),
#          recursive = FALSE)
# names(combs) <- sapply(combs, function(i) paste0(i, collapse = ""))
# str(combs)
# 
# library(prob)
# elements_male <- lapply(combs, function(i) setdiff(list_of_list_of_DE_genes_male[i], list_of_list_of_DE_genes_male[setdiff(names(list_of_list_of_DE_genes_male), i)]))
# 
# n.elements.male <- sapply(elements_male, length)
# print(n.elements.male)

## female
list_of_list_of_DE_genes_female <- list(
                              fd1 = fd1_markers_annotated_filtered$Gene.ID,
                              fd2 = fd2_markers_annotated_filtered$Gene.ID,
                              fd3 = fd3_markers_annotated_filtered$Gene.ID,
                              fd4 = fd4_markers_annotated_filtered$Gene.ID
                              )

list_of_list_of_DE_genes_male <- list(md3 = md3_markers_annotated_filtered$Gene.ID,
                              md4 = md4_markers_annotated_filtered$Gene.ID,
                              md5 = md5_markers_annotated_filtered$Gene.ID,
                              gd1 = gd1_markers_annotated_filtered$Gene.ID
                              )

library(gplots)

Attaching package: ‘gplots’

The following object is masked from ‘package:IRanges’:

    space

The following object is masked from ‘package:S4Vectors’:

    space

The following object is masked from ‘package:stats’:

    lowess

ItemsList_female <- venn(list_of_list_of_DE_genes_female, show.plot = FALSE)
ItemsList_male <- venn(list_of_list_of_DE_genes_male, show.plot = FALSE)

inspect

## access a combo
attributes(ItemsList_female)$intersections$`fd1:fd2`

 [1] "PBANKA-0207900" "PBANKA-0503300" "PBANKA-0504400" "PBANKA-0513900" "PBANKA-0517600" "PBANKA-0523700" "PBANKA-0523900"
 [8] "PBANKA-0602800" "PBANKA-0609500" "PBANKA-0611600" "PBANKA-0817400" "PBANKA-0820000" "PBANKA-0831000" "PBANKA-0902900"
[15] "PBANKA-0909500" "PBANKA-0934600" "PBANKA-0935600" "PBANKA-0941800" "PBANKA-0941900" "PBANKA-0943400" "PBANKA-1032100"
[22] "PBANKA-1035200" "PBANKA-1109300" "PBANKA-1117000" "PBANKA-1138900" "PBANKA-1205100" "PBANKA-1217600" "PBANKA-1239000"
[29] "PBANKA-1313100" "PBANKA-1318500" "PBANKA-1320800" "PBANKA-1352500" "PBANKA-1437600" "PBANKA-1463300"

# access a particular combo another way
# attributes(ItemsList_female)$intersections[i]
## access all combos
# names(attributes(ItemsList_female)$intersections)

make a long-format data frame summarising this

df_intersect_DE_female <- data.frame(matrix(ncol = 2))
names(df_intersect_DE_female) <- c("gene_id", "combination")
for (i in 1:length(names(attributes(ItemsList_female)$intersections))){
  df_1 <- data.frame(gene_id = attributes(ItemsList_female)$intersections[i], combination = names(attributes(ItemsList_female)$intersections)[i])
  df_intersect_DE_female <- rbind(df_intersect_DE_female, setNames(df_1, names(df_intersect_DE_female)))
}

## clean-up
##remove na row created at beginning
df_intersect_DE_female <- df_intersect_DE_female[!is.na(df_intersect_DE_female$gene_id), ]
## add in annotations
df_intersect_DE_female <- merge(gene_annotations, df_intersect_DE_female, by.x = "Gene.ID", by.y = "gene_id", all.y = TRUE, all.x = FALSE)
## add in screen phenotype
df_intersect_DE_female <-merge(df_intersect_DE_female ,screen_hits, by.x = "Gene.ID", by.y =  "new gene ID", all.x = TRUE, all.y = FALSE)

View(df_intersect_DE_female)

make a long-format data frame summarising this

df_intersect_DE_male <- data.frame(matrix(ncol = 2))
names(df_intersect_DE_male) <- c("gene_id", "combination")
for (i in 1:length(names(attributes(ItemsList_male)$intersections))){
  df_1 <- data.frame(gene_id = attributes(ItemsList_male)$intersections[i], combination = names(attributes(ItemsList_male)$intersections)[i])
  df_intersect_DE_male <- rbind(df_intersect_DE_male, setNames(df_1, names(df_intersect_DE_male)))
}

## clean-up
##remove na row created at beginning
df_intersect_DE_male <- df_intersect_DE_male[!is.na(df_intersect_DE_male$gene_id), ]
## add in annotations
df_intersect_DE_male <- merge(gene_annotations, df_intersect_DE_male, by.x = "Gene.ID", by.y = "gene_id", all.y = TRUE, all.x = FALSE)
## add in screen phenotype
df_intersect_DE_male <-merge(df_intersect_DE_male ,screen_hits, by.x = "Gene.ID", by.y =  "new gene ID", all.x = TRUE, all.y = FALSE)

View(df_intersect_DE_male)

What are the 12 genes that overlap all 3 knockouts?

head(df_intersect_DE_male[df_intersect_DE_male$combination == "md4:md5:gd1", ])

inspect the genes of interest and see how it changes in the DE analyses

## DEAD/DEAH box helicase = PBANKA-0312700 
## ALBA1 = PBANKA-1423300
gene <- "PBANKA-1423300"
df_gene_of_interest_results <- data.frame(matrix(ncol=ncol(list_of_DE_gene_lists[[4]])))
df_gene_of_interest_results$mutant_de <- NA
names(df_gene_of_interest_results) <- c(names(list_of_DE_gene_lists[[4]]), "mutant_de")
for (i in 1:length(list_of_DE_gene_lists)){
  if (nrow(list_of_DE_gene_lists[[i]][list_of_DE_gene_lists[[i]]$Gene.ID %in% gene, ]) > 0){
    df_1 <- list_of_DE_gene_lists[[i]][list_of_DE_gene_lists[[i]]$Gene.ID %in% gene, ]
    df_1$mutant_de <- names(list_of_DE_gene_lists)[i]
    names(df_1) <- c(names(list_of_DE_gene_lists[[4]]), "mutant_de")
    df_gene_of_interest_results <- rbind(df_gene_of_interest_results, df_1)
  }
}
df_gene_of_interest_results

Then visualise

## split violin plot for each mutant and their wild-type
## define functions as from: https://stackoverflow.com/questions/35717353/split-violin-plot-with-ggplot2
GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, 
                           draw_group = function(self, data, ..., draw_quantiles = NULL) {
  data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
  grp <- data[1, "group"]
  newdata <- plyr::arrange(transform(data, x = if (grp %% 2 == 1) xminv else xmaxv), if (grp %% 2 == 1) y else -y)
  newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
  newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"])

  if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
    stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <=
      1))
    quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
    aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
    aesthetics$alpha <- rep(1, nrow(quantiles))
    both <- cbind(quantiles, aesthetics)
    quantile_grob <- GeomPath$draw_panel(both, ...)
    ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
  }
  else {
    ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
  }
})

geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., 
                              draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, 
                              show.legend = NA, inherit.aes = TRUE) {
  layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, 
        position = position, show.legend = show.legend, inherit.aes = inherit.aes, 
        params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}

## extract data:
## get clusters of interest:
## paths
# male:  c(5, 10, 4, 9, 12, 16)
# female: c(14, 15, 13, 8, 11, 7, 0)
# both: c(1, 2, 3, 6)
df_violin <- tenx.mutant.integrated.sex@meta.data
df_violin <- df_violin[df_violin$seurat_clusters %in% c(14, 15, 13, 8, 11, 7, 0), ]
## get genotypes of interest:
## cluster compared
## male
# md3 - 4
# md4 - 12
# md5 - 10
# gd1 - 12 (used) or 16 (seems to be it's own cluster)?
## female
# fd1 - 8
# fd2 - has it's own cluster of 15 -> compare 15 to 8
# fd3 - 0 - not many cells
# fd4 - 0 - not many cells
df_violin <- df_violin[df_violin$identity_combined %in% c("wild-type (10x)", "wild-type (Smart-seq2)", "fd1", "fd2", "fd3", "fd4"), ]

## extract expression values
df_1 <- as.data.frame(t(tenx.mutant.integrated.sex@assays$RNA@data))
df_1 <- df_1[rownames(df_1) %in% rownames(df_violin), ]
df_violin <- merge(df_violin, df_1, by.x = 0, by.y = 0)

pt_plot_a <- ggplot(df_violin, aes(x = pt_values_branch_all, y = `PBANKA-0312700`, colour = identity_combined)) + 
  geom_point() + 
  #scale_y_continuous(limit=c(0.00001,NA),oob=censor) +
  geom_smooth(method = "gam", formula = y ~s(x)) +
  theme_classic() +
  ylim(-1, 6) + 
  stat_cor(method = "spearman", 
           aes(color = identity_combined), 
           label.x.npc = 'left', 
    label.y.npc = "top") +
  labs(x = "Pseudotime", y = "Expression level", title = "DEAD/DEAH box helicase (PBANKA_0312700)") +
  scale_fill_discrete_qualitative(palette = "Set 3")+
  scale_colour_discrete_qualitative(palette = "Set 3") +
  theme(legend.position = "right", plot.title = element_text(face = "italic"))

pt_plot_a

pt_plot_b <- ggplot(df_violin, aes(x = pt_values_branch_all, y = `PBANKA-0827500`, colour = identity_combined)) + 
  geom_point() + 
  #scale_y_continuous(limit=c(0.00001,NA),oob=censor) +
  geom_smooth(method = "gam", formula = y ~s(x)) +
  theme_classic() +
  ylim(-1, 6) + 
  stat_cor(method = "spearman", 
           aes(color = identity_combined), 
           label.x.npc = 'left', 
    label.y.npc = "top") +
  labs(x = "Pseudotime", y = "Expression level", title = "glutamine-dependent NAD(+) synthetase (PBANKA_0827500)") +
  scale_fill_discrete_qualitative(palette = "Set 3")+
  scale_colour_discrete_qualitative(palette = "Set 3") +
  theme(legend.position = "right", plot.title = element_text(face = "italic"))

pt_plot_b

## cluster compared
## male
# md3 - 4
# md4 - 12
# md5 - 10
# gd1 - 12 (used) or 16 (seems to be it's own cluster)?
## female
# fd1 - 8
# fd2 - has it's own cluster of 15 -> compare 15 to 8
# fd3 - 0 - not many cells
# fd4 - 0 - not many cells

## extract cells of interest
df_violin$violin_group <- NA
df_violin[which(df_violin$identity_combined %in% c("wild-type (10x)", "wild-type (Smart-seq2)", "fd1") & df_violin$seurat_clusters %in% c(8)), ]$violin_group <- "fd1"
df_violin[which(df_violin$identity_combined %in% c("wild-type (10x)", "wild-type (Smart-seq2)", "fd2") & df_violin$seurat_clusters %in% c(8, 15) & df_violin$pt_values_branch_all < 40), ]$violin_group <- "fd2"
df_violin[which(df_violin$identity_combined %in% c("wild-type (10x)", "wild-type (Smart-seq2)", "fd3") & df_violin$seurat_clusters %in% c(0)), ]$violin_group <- "fd3"
df_violin[which(df_violin$identity_combined %in% c("wild-type (10x)", "wild-type (Smart-seq2)", "fd4") & df_violin$seurat_clusters %in% c(0)), ]$violin_group <- "fd4"
df_violin_subset <-df_violin[!is.na(df_violin$violin_group), ]

## plot
ggplot(df_violin_subset, aes(violin_group, `PBANKA-0312700`, fill = genotype_combined)) + geom_split_violin()

3. Look at highest fold-change

## make a list of list of marker genes
list_of_DE_gene_lists <- list(md3 = md3_markers_annotated_filtered,
                              md4 = md4_markers_annotated_filtered,
                              md5 = md5_markers_annotated_filtered,
                              gd1 = gd1_markers_annotated_filtered,
                              fd1 = fd1_markers_annotated_filtered,
                              fd2 = fd2_markers_annotated_filtered,
                              fd3 = fd3_markers_annotated_filtered,
                              fd4 = fd4_markers_annotated_filtered
                              )

## make a blank list to save results to
top_of_DE_genes <- list()

## for loop that finds screen hits in DE lists
for (i in 1:length(list_of_DE_gene_lists)){
  top_of_DE_genes[[i]] <- list_of_DE_gene_lists[[i]][list_of_DE_gene_lists[[i]]$avg_logFC > log(4) | list_of_DE_gene_lists[[i]]$avg_logFC < log(0.25), ]
  if (nrow(top_of_DE_genes[[i]]) > 0){
  top_of_DE_genes[[i]]$mutant <- names(list_of_DE_gene_lists)[i]
  }
}

## then bind these all together in one dataframe for ease of visualisation and view
top_DE_genes <- data.frame(matrix(ncol=13))
names(top_DE_genes) <- names(top_of_DE_genes[[2]])
for (i in 1:length(list_of_DE_gene_lists)){
  if (nrow(top_of_DE_genes[[i]]) == 0) next
  top_DE_genes <- rbind(top_DE_genes, top_of_DE_genes[[i]])
}
## remove the na row that is added
top_DE_genes <- top_DE_genes[!is.na(top_DE_genes$Gene.ID), ]

View(top_DE_genes)

MALE VISUALISE

## extract data:
## get clusters of interest:
## paths
# male:  c(5, 10, 4, 9, 12, 16)
# female: c(14, 15, 13, 8, 11, 7, 0)
# both: c(1, 2, 3, 6)
df_violin <- tenx.mutant.integrated.sex@meta.data
df_violin <- df_violin[df_violin$seurat_clusters %in% c(5, 10, 4, 9, 12, 16), ]
## get genotypes of interest:
## cluster compared
## male
# md3 - 4
# md4 - 12
# md5 - 10
# gd1 - 12 (used) or 16 (seems to be it's own cluster)?
## female
# fd1 - 8
# fd2 - has it's own cluster of 15 -> compare 15 to 8
# fd3 - 0 - not many cells
# fd4 - 0 - not many cells
df_violin <- df_violin[df_violin$identity_combined %in% c("wild-type (10x)", "wild-type (Smart-seq2)", "md3", "md4", "md5", "gd1"), ]

## extract expression values
df_1 <- as.data.frame(t(tenx.mutant.integrated.sex@assays$RNA@data))
df_1 <- df_1[rownames(df_1) %in% rownames(df_violin), ]
df_violin <- merge(df_violin, df_1, by.x = 0, by.y = 0)

pt_plot_b <- ggplot(df_violin, aes(x = pt_values_branch_all, y = `PBANKA-1206900`, colour = identity_combined)) + 
  geom_point() + 
  #scale_y_continuous(limit=c(0.00001,NA),oob=censor) +
  geom_smooth(method = "gam", formula = y ~s(x)) +
  theme_classic() +
  ylim(-1, 6) + 
  stat_cor(method = "spearman", 
           aes(color = identity_combined), 
           label.x.npc = 'left', 
    label.y.npc = "top") +
  labs(x = "Pseudotime", y = "Expression level", title = "tubulin beta chain (PBANKA_1206900)") +
  scale_fill_discrete_qualitative(palette = "Set 3")+
  scale_colour_discrete_qualitative(palette = "Set 3") +
  theme(legend.position = "right", plot.title = element_text(face = "italic"))

pt_plot_b

pt_plot_b <- ggplot(df_violin, aes(x = pt_values_branch_all, y = `PBANKA-0211500`, colour = identity_combined)) + 
  geom_point() + 
  #scale_y_continuous(limit=c(0.00001,NA),oob=censor) +
  geom_smooth(method = "gam", formula = y ~s(x)) +
  theme_classic() +
  ylim(-1, 6) + 
  stat_cor(method = "spearman", 
           aes(color = identity_combined), 
           label.x.npc = 'left', 
    label.y.npc = "top") +
  labs(x = "Pseudotime", y = "Expression level", title = "md4 non-expresing (PBANKA_0211500)") +
  scale_fill_discrete_qualitative(palette = "Set 3")+
  scale_colour_discrete_qualitative(palette = "Set 3") +
  theme(legend.position = "right", plot.title = element_text(face = "italic"))

pt_plot_b

pt_plot_b <- ggplot(df_violin, aes(x = pt_values_branch_all, y = `PBANKA-1423300`, colour = identity_combined)) + 
  geom_point() + 
  #scale_y_continuous(limit=c(0.00001,NA),oob=censor) +
  geom_smooth(method = "gam", formula = y ~s(x)) +
  theme_classic() +
  ylim(-1, 6) + 
  stat_cor(method = "spearman", 
           aes(color = identity_combined), 
           label.x.npc = 'left', 
    label.y.npc = "top") +
  labs(x = "Pseudotime", y = "Expression level", title = "ALBA1 (PBANKA_1423300)") +
  scale_fill_discrete_qualitative(palette = "Set 3")+
  scale_colour_discrete_qualitative(palette = "Set 3") +
  theme(legend.position = "right", plot.title = element_text(face = "italic"))

pt_plot_b

3. GO-term enrichment

MCA Core Genes

dot_plot_male <- DotPlot(mutant_male_seurat, assay = "RNA", features = mca_male_core_genes, group.by = "identity_combined") +
  theme_classic() +
  # change appearance and remove axis elements, and make room for arrows
  theme(axis.text.x = element_text(size=16, angle = 45, hjust=1,vjust=1, family = "Arial"), text=element_text(size=16, family="Arial"), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical", plot.title = element_blank(), plot.margin = unit(c(1,3,1,3), "lines")) +
  #change the colours
  scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white", begin = 0, end = 1, direction = 1) +
  ## change x axis label
  labs(x = "Malaria Cell Atlas Core Male Genes", y = "Genotype", title = "Expression of Male MCA Core Genes in Male Mutant Cells") +
  ## change label on bottom of plot so we can indicate markers
  scale_y_discrete(labels = c("gd1", "md3", "md4", "md5", "WT (SS2)")) +
  scale_x_discrete(labels = gsub("-", "_", mca_male_core_genes)) +
  coord_flip()

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

## view
print(dot_plot_male)


## plot female
dot_plot_female <- DotPlot(mutant_female_seurat, assay = "RNA", features = mca_female_core_genes, group.by = "identity_combined") +
  theme_classic() +
  # change appearance and remove axis elements, and make room for arrows
  theme(axis.text.x = element_text(size=16, angle = 45, hjust=1,vjust=1, family = "Arial"), text=element_text(size=16, family="Arial"), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical", plot.title = element_blank(), plot.margin = unit(c(1,3,1,3), "lines")) +
  #change the colours
  scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white", begin = 0, end = 1, direction = 1) +
  ## change x axis label
  labs(x = "Malaria Cell Atlas Core Female Genes", y = "Genotype", title = "Expression of Female MCA Core Genes in Feale Mutant Cells") +
  ## change label on bottom of plot so we can indicate markers
  scale_y_discrete(labels = c("fd1", "fd2", "fd3", "fd4", "WT (SS2)")) +
  scale_x_discrete(labels = gsub("-", "_", mca_female_core_genes)) +
coord_flip()

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

## view
print(dot_plot_female)

save

ggsave("../images_to_export/GCSKO_sexbranch_dot_male_v2.png", plot = dot_plot_male, device = "png", path = NULL, scale = 1, width = 20, height = 60, units = "cm", dpi = 300, limitsize = TRUE)

ggsave("../images_to_export/GCSKO_sexbranch_dot_female_v2.png", plot = dot_plot_female, device = "png", path = NULL, scale = 1, width = 20, height = 60, units = "cm", dpi = 300, limitsize = TRUE)

Run tSNE

tenx.mutant.integrated.sex <- RunTSNE(tenx.mutant.integrated.sex, seed.use = 1234, perplexity = 200)
DimPlot(tenx.mutant.integrated.sex, reduction = "tsne", pt.size = 0.01, label = TRUE)

B. UMAP

calculate UMAP

## run UMAP
#tenx.mutant.integrated.sex <- RunUMAP(tenx.mutant.integrated.sex, reduction = "pca", dims = 1:15, n.neighbors = 20, seed.use = 1234, min.dist = 0.5, repulsion.strength = 0.05, reduction.name = "umapoptimised_post_repca")

tenx.mutant.integrated.sex <- RunUMAP(tenx.mutant.integrated.sex, reduction = "pca", dims = 1:11, seed.use = 1234, n.neighbors = 10, reduction.name = "umapoptimised_post_repca", reduction.key='umap_post_repca_')

## plot
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "identity_combined") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, split.by = "genotype_combined") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, split.by = "experiment", group.by = "identity_combined") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

FeaturePlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", pt.size = 0.01, features = "PBANKA-1319500") + coord_fixed()

check markers to orientate

plots <- FeaturePlot(tenx.mutant.integrated.sex, features = c("PBANKA-1319500", "PBANKA-0416100"), blend = TRUE, combine = FALSE, coord.fixed = TRUE, reduction = "umapoptimised_post_repca")

plots[[3]] + NoLegend()  # Get just the co-expression plot, built-in legend is meaningless for this plot
plots[[4]] # Get just the key
CombinePlots(plots[3:4], legend = 'none', ncol =2, nrow = 1, rel_widths = c(2, 1), rel_heights = c(4,1)) # Stitch the co-expression and key plots together

inspect mutants in 13 that are in between sexes

## get mutant cells
mutant_sex_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == "Mutant"),])
                                                        
## subset object
mutant_only_seurat <- subset(tenx.mutant.integrated.sex, cells = mutant_sex_cells)

## plot
umap_mutant <- DimPlot(mutant_only_seurat, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

## view
HoverLocator(plot = umap_mutant, information = FetchData(mutant_only_seurat, vars = c("ident", "identity_updated", "nFeature_RNA", "identity_combined")))

DimPlot(mutant_only_seurat, label = TRUE, label.size = 8, repel = FALSE, pt.size = 0.5, dims = c(2,1), reduction = "DIM_UMAP") + 
     coord_fixed() +
     theme(legend.position="bottom", 
           axis.line=element_blank(),
           axis.text.x=element_blank(),
           axis.text.y=element_blank(),
           axis.ticks=element_blank(),
           axis.title.x=element_blank(),
           axis.title.y=element_blank()) + 
     guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))

4. Clustering

Investigate sub clustering of pre-branch clusters

Ultimately, we are looking for coexpression after the expression of AP2G:

## plot
marker_gene_plot_AP2G <- FeaturePlot(tenx.mutant.integrated.sex, features = "PBANKA-1437500", coord.fixed = TRUE, min.cutoff = "q1", dims = c(2,1), reduction = "DIM_UMAP", pt.size = 1, order = TRUE) + 
  theme_void() + 
  labs(title = paste("AP2G Expression")) + 
  theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
  scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

##view
marker_gene_plot_AP2G

and we have the following clusters currently:

## Plot
umap_cluster <- DimPlot(tenx.mutant.integrated.sex, label = TRUE, label.size = 8, repel = FALSE, pt.size = 0.5, dims = c(2,1), reduction = "DIM_UMAP") + 
  coord_fixed() +
  theme(legend.position="bottom", 
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()) + 
  guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))

## print
umap_cluster

11 is the common sex branch so we will interrogate that branch

## subset cluster 13 cells
cells_bipot <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 11 & tenx.mutant.integrated.sex@meta.data$genotype_combined == "WT", ])

## subset cluster 13
seurat_bipot <- subset(tenx.mutant.integrated.sex, cells = cells_bipot)
seurat_bipot

## re-PCA
seurat_bipot <- RunPCA(seurat_bipot, npcs = 30, verbose = FALSE)
ElbowPlot(seurat_bipot, ndims = 30, reduction = "pca")

## recluster
seurat_bipot <- FindNeighbors(seurat_bipot, dims = 1:15)
seurat_bipot <- FindClusters(seurat_bipot, resolution = 1, random.seed = 42, algorithm = 2)
DimPlot(seurat_bipot, reduction = "pca",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") 

## plot cluster resolution = 1
## plot
DimPlot(seurat_bipot, reduction = "DIM_UMAP",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

2 and 1 are superimposed on the branch, lets find markers for each cluster

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

# PBANKA-1437500 - AP2G - commitment

## find markers
seurat_bipot_markers <- FindAllMarkers(seurat_bipot, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST")

## inspect result
markers_subset <- seurat_bipot_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset

## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant

## plot
VlnPlot(seurat_bipot, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")

Specifically look for markers between 0 and 2

cluster_1_2_markers <- FindMarkers(seurat_bipot, ident.1 = 0, ident.2 = 2, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)

cluster_1_2_markers_subset <- cluster_1_2_markers %>% filter(p_val_adj < 0.05)
cluster_1_2_markers_subset <- cluster_1_2_markers_subset[order(-cluster_1_2_markers_subset$avg_logFC), ]
cluster_1_2_markers_subset

## this is no longer needed as only wild-type cells are chosen.
## Just check that mutants aren't interfering  with the analysis i.e. the clusters don't just belong to a certain genotype or mutant cell
#table(seurat_bipot$seurat_clusters, seurat_bipot$identity_combined)

Do the same analysis on cluster 5 quickly

## subset cluster 13 cells
cells_three <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 5, ])

## subset cluster 13
cells_three <- subset(tenx.mutant.integrated.sex, cells = cells_three)

## re-PCA
cells_three <- RunPCA(cells_three, npcs = 30, verbose = FALSE)
ElbowPlot(cells_three, ndims = 30, reduction = "pca")

## recluster
cells_three <- FindNeighbors(cells_three, dims = 1:5)
## first do a c(0.5,1,2) for resolution and then pick a sensible cluster number
cells_three <- FindClusters(cells_three, resolution = 0.5, random.seed = 42, algorithm = 2)
DimPlot(cells_three, reduction = "pca",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") 

## plot cluster resolution = 1
## plot
DimPlot(cells_three, reduction = "DIM_UMAP",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

# PBANKA-1437500 - AP2G - commitment

## find markers
cells_three_markers <- FindAllMarkers(cells_three, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)

## inspect result
markers_subset <- cells_three_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset

## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant

## plot
VlnPlot(cells_three, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")

markers_subset_annotated <- merge(markers_subset, gene_annotations,  by.x = "gene", by.y = "Gene.ID", all = FALSE)
markers_subset_annotated

Cluster 2 contains the male cells as this has md1 and ap2g in it.

cluster 3 has histone h2b, histone 4, actin 1, so likley contains asexual cells

cluster 0 is potentially female as these genes have female-biased expression in the kasia data

cluster 1 genes were not screened so unknown identity

Then look at the co-expression of AP2-G and MD1

cluster_2_cells <- rownames(cells_three@meta.data[cells_three@meta.data$seurat_clusters == "2",])
cells_three_2 <- subset(cells_three, cells = cluster_2_cells)

df_plot <- data.frame(t(data.frame(cells_three_2@assays$RNA[c("PBANKA-1302700", "PBANKA-1437500"), ])))

ggplot(df_plot, aes(PBANKA.1302700, PBANKA.1437500)) + geom_point()

rm(cells_three_2)

Do the same on cluster 7

## subset cluster 13 cells
cells_seven <- rownames(tenx.mutant.integrated.sex@meta.data[tenx.mutant.integrated.sex@meta.data$seurat_clusters == 7 & tenx.mutant.integrated.sex@meta.data$genotype_combined == "WT", ])

## subset cluster 13
cells_seven <- subset(tenx.mutant.integrated.sex, cells = cells_bipot)
cells_seven

## re-PCA
cells_seven <- RunPCA(cells_seven, npcs = 30, verbose = FALSE)
ElbowPlot(cells_seven, ndims = 30, reduction = "pca")

## recluster
cells_seven <- FindNeighbors(cells_seven, dims = 1:15)
cells_seven <- FindClusters(cells_seven, resolution = 1, random.seed = 42, algorithm = 2)
DimPlot(cells_seven, reduction = "pca",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") 

## plot cluster resolution = 1
## plot
DimPlot(cells_seven, reduction = "DIM_UMAP",  dims = c(2,1), label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "seurat_clusters") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

# PBANKA-0828000         GCSKO-3  GD1

# PBANKA-1302700       GCSKO-oom  MD1 
# PBANKA-1447900        GCSKO-29  MD2
# PBANKA-0102400         GCSKO-2  MD3 
# PBANKA-0716500        GCSKO-19  MD4 
# PBANKA-0413400    GCSKO-10_820  MD5

# PBANKA-1454800        GCSKO-21  FD1
# PBANKA-0902300        GCSKO-13  FD2
# PBANKA-1418100        GCSKO-17  FD3   
# PBANKA-1435200        GCSKO-20  FD4 

# PBANKA-1437500 - AP2G - commitment

## find markers
cells_seven_markers <- FindAllMarkers(cells_seven, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)

## inspect result
markers_subset <- cells_seven_markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_logFC) %>% filter(p_val_adj < 0.05)
markers_subset <- markers_subset[order(-markers_subset$avg_logFC), ]
markers_subset

## find markers
markers_subset_mutant <- markers_subset[which(markers_subset$gene %in% list_of_mutant_genes), ]
markers_subset_mutant

## plot
#VlnPlot(cells_seven, features = c(markers_subset_mutant$gene, "PBANKA-1437500"), assay = "RNA")

# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# 
# BiocManager::install("slingshot")
library(slingshot)
library(RColorBrewer)

## extracts only 10x cells 
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"),])
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X" & !tenx.mutant.integrated.sex@meta.data$seurat_clusters == "7"),])


## make a new Seurat of this
seurat.object <-subset(tenx.mutant.integrated.sex, cells = wt_cells)

## get UMAP coordinates
umap_coords <- seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings
## old code: seurat.object@reductions$umapoptimised_post_repca@cell.embeddings
## new code: seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings

## get clusters
#clusters <- as.list(seurat.object@meta.data$integrated_snn_res.4)
#names(clusters) <- rownames(seurat.object@meta.data)
#clusters <- as.list(clusters)
## cluster using kmeans
## you need to set a seed here to ensure the results are reproducible
set.seed(42)
clusters <- kmeans(umap_coords, centers = 13)$cluster

## plot
## make a nicer plot so we can interpret the clusters
df_plotting <- as.data.frame(cbind(umap_coords, clusters))
## change to character to make it discrete
df_plotting$clusters <- as.character(df_plotting$clusters)
## plot
ggplot(df_plotting, aes(x = DIMUMAP_1, y = DIMUMAP_2, colour = clusters)) + 
  geom_point() + 
  scale_colour_manual(values = rainbow(15)) + 
  theme_classic()


## initialise plot to prevent error
plot.new()

## slingshot to get lineages
lineage_uamp <- getLineages(umap_coords, clusters, start.clus = '5', end.clus = c('3', '16'))

## make a curve through lineage 
crv1 <- getCurves(lineage_uamp)

## set up the colour pal
nb.cols <- 18 
mycolors <- colorRampPalette(brewer.pal(9, "Set1"))(nb.cols)

## join points with line segments and plot
plot(umap_coords, col = mycolors[clusters], asp = 3, pch = 16)
lines(crv1, lwd = 3, col = 'black')

Visualise cells:

## define cells
slingshot_results_df <- as.data.frame(slingPseudotime(crv1))
curve_one_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve1), ])
curve_two_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve2), ])
curve_three_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve3), ])
curve_four_cells <- rownames(slingshot_results_df[!is.na(slingshot_results_df$curve4), ])

c1_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_one_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

c2_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_two_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

c3_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_three_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

c4_plot <- DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = curve_four_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

plot_grid(c1_plot, c2_plot, c3_plot, c4_plot, labels = c('curve 1', 'curve 2', 'curve 3', 'curve 4'), label_size = 12, 
                                ncol = 2, 
                                nrow=2
                            
                )

Add data to Seurat:

## extract data to add to Seurat
## extract clusters
meta_data_to_add_from_slingshot <- data.frame(clusters_k_means_UMAP = clusters)
## Add pseudotimes
# check the length of each branch to see which curve is which using: sum(is.na(as.data.frame(slingPseudotime(crv1))$curve1))
# then inspect using the ggplot2 above to where males are - 
# tail(as.data.frame(slingPseudotime(crv1)), 100)
# tail(meta_data_to_add_from_slingshot, 100)
meta_data_to_add_from_slingshot$PT_Female_UMAP <- as.data.frame(slingPseudotime(crv1))$curve1
meta_data_to_add_from_slingshot$PT_Male_UMAP <- as.data.frame(slingPseudotime(crv1))$curve2
## add designation to SCE object
meta_data_to_add_from_slingshot$sex_UMAP <- "pre-det"
meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Female_UMAP))] <- "male"
meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Male_UMAP))] <- "female"

## if there are 3 curves in slingPseudotime(crv1):
#meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Female_UMAP) & is.na(as.data.frame(slingPseudotime(crv1))$curve3))] <- "male"
#meta_data_to_add_from_slingshot$sex_UMAP[which(is.na(meta_data_to_add_from_slingshot$PT_Male_UMAP) & is.na(as.data.frame(slingPseudotime(crv1))$curve3))] <- "female"

## add clusters to SCE object
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, meta_data_to_add_from_slingshot)

## plot
FeaturePlot(tenx.mutant.integrated.sex, label.size = 5, pt.size = 0.5, features = c("PT_Female_UMAP", "PT_Male_UMAP")) +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

plot sex designations

DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "sex_UMAP", na.value = "white") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

6. Monocle on sex cells

calculate pseudotime and modules

Preparation

## extracts only 10x cells and also remove cluster 0 cells 
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X" & !tenx.mutant.integrated.sex@meta.data$seurat_clusters == "7"),])

## make a new Seurat of this
seurat.object <-subset(tenx.mutant.integrated.sex, cells = wt_cells)

## check that this is the same as the pb_sex_filtered object
#data_test <- as(as.matrix(GetAssayData(pb_sex_filtered, assay = "RNA", slot = "data")), 'sparseMatrix')
#is.equal
#is.identical

## extract counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
## with 10x only cells: all.equal(as.data.frame(counts(monocle.object)), as.data.frame(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "counts")))) returns TRUE and also returns true when you change the slot input to "counts" and use monocle to norm it with method "log"
data <- as(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
## make phenodata
pd <- data.frame(seurat.object@meta.data)
## keep only the columns that are relevant in metadata
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
## make gene metadata
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)

## preprocess
monocle.object = preprocess_cds(monocle.object, num_dim = 50, norm_method = "none")
### if using integrated data:
# norm_method = "none", alignment_group = "~ experiment"

## plot jack straw plot
#plot_pc_variance_explained(monocle.object)

#monocle.object = reduce_dimension(monocle.object, reduction_method = "UMAP", preprocess_method = "PCA", umap.metric = "euclidean", umap.n_neighbors = 50, umap.min_dist = 0.5, verbose = FALSE)

#plot_cells(monocle.object, color_cells_by="experiment")

## graph learning

## add UMAP from Seurat
monocle.object@int_colData@listData$reducedDims@listData[["UMAP"]] <- seurat.object@reductions[["DIM_UMAP"]]@cell.embeddings
## if you want the old UMAP from the original all cells one, use: "DIM_UMAP"

## cluster
## this is essential to run the learn_graph function later on
monocle.object = cluster_cells(monocle.object)

## plot initial clustering by monocle
#plot_cells(monocle.object, color_cells_by="cluster", group_cells_by="partition", x = 2, y = 1)

## map pseudotime
monocle.object = learn_graph(monocle.object, learn_graph_control=list(ncenter=250, minimal_branch_len = 30), use_partition = FALSE)
# learn_graph_control=list(ncenter=500) - play with this parameter - lower tends to give fewer branches and higher tends to give more
# 500 - old analysis
# 250 - new analysis 

## Plot cells
plot_cells(monocle.object, color_cells_by="partition", group_cells_by="partition", x = 2, y = 1)

Define identities of cells

male

monocle.object_male <- choose_graph_segments(monocle.object)

female

monocle.object_female <- choose_graph_segments(monocle.object)

bipotential

monocle.object_bipot <- choose_graph_segments(monocle.object)

asexual

monocle.object_asex <- choose_graph_segments(monocle.object)

asexual fate

monocle.object_asex_fate <- choose_graph_segments(monocle.object)

check

df_freq <- data.frame(table(c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))))
paste("number of cells in seurat object is", length(colnames(monocle.object)), ". The number of cells selected here with an identitity is", dim(df_freq)[1])
df_freq <- df_freq[df_freq$Freq > 1, ]
df_freq

Inspect where these missing cells are:

'%ni%' <- Negate('%in%')

not_assigned_cells <- colnames(monocle.object)[colnames(monocle.object) %ni% c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))]

DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = not_assigned_cells, dims = c(2,1), reduction = "DIM_UMAP") +
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e"))

## create annotation dataframe from these results:
df_monocle_sexes <- rbind(data.frame("cell_name" = colnames(monocle.object_male), "sex" = rep("Male", length(colnames(monocle.object_male)))),
                          data.frame("cell_name" = colnames(monocle.object_female), "sex" = rep("Female", length(colnames(monocle.object_female)))),
                          data.frame("cell_name" = colnames(monocle.object_bipot), "sex" = rep("Bipotential", length(colnames(monocle.object_bipot)))),
                          data.frame("cell_name" = colnames(monocle.object_asex), "sex" = rep("Asexual", length(colnames(monocle.object_asex)))),
                          data.frame("cell_name" = colnames(monocle.object_asex_fate), "sex" = rep("Asexual_Fate", length(colnames(monocle.object_asex_fate)))),
                          data.frame("cell_name" = not_assigned_cells, "sex" = rep("Unassigned", length(not_assigned_cells)))
                          )

dim(df_monocle_sexes)

## order like the metadata
df_monocle_sexes <- df_monocle_sexes[match(rownames(monocle.object@colData), df_monocle_sexes$cell_name), ]

## add this back into the monocle object
monocle.object@colData$Sexes_monocle <- df_monocle_sexes$sex

Pseudotime Calculation

## Order the cells and calculate pseudotime
monocle.object = order_cells(monocle.object)

## Plot
umap_pt <- plot_cells(monocle.object, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 2, y = 1, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
  coord_fixed() +
  theme_void() +
  labs(title = "Pseudotime") +
  theme(plot.title = element_text(hjust = 0.5))

## view
umap_pt

save

ggsave("../images_to_export/GCSKO_sexbranch_umap_pt.png", plot = umap_pt, device = "png", path = NULL, scale = 1, width = 10, height = 10, units = "cm", dpi = 300, limitsize = TRUE)

Just check if the ‘hook’ on the males is a lineage or dead/dying/activated gams, or mutants

FeaturePlot(tenx.mutant.integrated.sex, features = "PBANKA-0416100", coord.fixed = TRUE, min.cutoff = "q1", dims = c(2,1), reduction = "DIM_UMAP", pt.size = 1, order = TRUE) + 
            theme_void() + 
            labs(title = paste("MG1 (Male)")) + 
            theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) + 
            scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))

It appears MG1 marker expression is decreasing/off in these cells - inspect mutant status

plot <- DimPlot(tenx.mutant.integrated.sex, label = FALSE, label.size = 8, repel = FALSE, pt.size = 0.05, dims = c(2,1), reduction = "DIM_UMAP", group.by = "identity_updated") + 
  coord_fixed() +
  theme(legend.position="bottom", 
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()) + 
  guides(colour=guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size=5)))

HoverLocator(plot = plot, information = FetchData(tenx.mutant.integrated.sex, vars = c("ident", "identity_updated", "nFeature_RNA")))

Check how well it correlates with the original pseudotime

when pseudotime was calculated on the whole object

library(ggpubr)
## extract pseudotime values:
pt_values_new <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
pt_values_new$cell_name <- rownames(pt_values_new)
meta_data_df <- as.data.frame(monocle.object@colData)
meta_data_df$cell_name <- rownames(meta_data_df)
meta_data_df <- merge(meta_data_df, pt_values_new, by = "cell_name")
names(meta_data_df)[ncol(meta_data_df)]<- "pt"

ggplot(meta_data_df, aes(x = old_pt_values, y = pt, colour = cluster_colours_figure)) + 
  geom_point() +  
  geom_smooth(method = "lm", se = FALSE) +
  theme_classic() + stat_cor(method = "pearson")

This shows very good correlation.

Isolate Branches

## access the closest principal graph node vertex for each cell and assign it as a column in your colData table using
#colData(monocle.object)$closest_vertex <- monocle.object@principal_graph_aux[["UMAP"]]$pr_graph_cell_proj_closest_vertex[,1]

## plot
#plot_cells(monocle.object, color_cells_by = "closest_vertex", label_cell_groups = FALSE)

Module Construction

## find genes that change as a function of pt:
monocle.object_pr_test_res <- graph_test(monocle.object, neighbor_graph="principal_graph", cores=8)

## find significant genes
## I used 0.05 previously in all cells 
## 2884 w. p = 0.01, 3260 w. p = 0.05
pr_deg_ids <- row.names(subset(monocle.object_pr_test_res, q_value < 0.05))

## collect into modules
gene_module_df_sex <- find_gene_modules(monocle.object[pr_deg_ids,], resolution=c(10^seq(-6,2)), random_seed = 1234)

## how many genes in modules?
dim(gene_module_df_sex)

Plot Modules

General Module Composition

Make a dataframe to plot by aggregating clusters vs. modules

## make cell group df
cell_group_df <- tibble::tibble(cell=row.names(colData(monocle.object)), cell_group=colData(monocle.object)$seurat_clusters)

## make plotting df
agg_mat <- aggregate_gene_expression(monocle.object, gene_module_df_sex, cell_group_df)

Find out how many genes there are per total so we can add this to the plot

## how many genes per module?
genes_per_module <- as.data.frame(table(gene_module_df_sex$module))
genes_per_module

Find out which modules our mutant genes reside in

## create a list of our mutant gene IDs
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

## make a dataframe to convert the gene IDs into actual IDs
df_mutant_ids <- as.data.frame(unique(cbind(tenx.mutant.integrated@meta.data$identity_gene_updated, tenx.mutant.integrated@meta.data$identity_updated, tenx.mutant.integrated@meta.data$identity_name_updated)))[-c(1, 3),]
# remove the "820" bit on 10
df_mutant_ids$V1 <- gsub("_820", "", df_mutant_ids$V1)
# change the underscore (_) to a dash (-) in gene IDs
df_mutant_ids$V1 <- gsub("_", "-", df_mutant_ids$V1)
names(df_mutant_ids) <- c("gene_ID", "mutant_identity", "mutant_identity_2")

## subset modules df to include only mutant gene IDs
df_mutant_gene_modules <- as.data.frame(gene_module_df_sex[which(gene_module_df_sex$id %in% list_of_mutant_genes),])
names(df_mutant_gene_modules)[1] <- "gene_ID"

## merge dataframes
df_mutant_gene_modules <- merge(df_mutant_gene_modules, df_mutant_ids, by = "gene_ID")

## Inspect
df_mutant_gene_modules

so modules of interest are:

table(df_mutant_gene_modules$module)

Which modules do other genes of interest lie in?:

## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
# ccp2 - "PBANKA-1319500" - female 820
# p25 - "PBANKA-0515000" - female
# p28 - "PBANKA-0514900" - female
# ccp3 - "PBANKA-1035200" - female -  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909122/
# nek4 - "PBANKA-0616700" - female
# ap2-fg - "PBANKA-1415700" - female 
# dozi - "PBANKA-1217700" - female
# MG1 - "PBANKA-0416100" - male 820
# hap2 - "PBANKA-1212600" - male
# MAPK2 - "PBANKA-0933700" - male
# nek1 - "PBANKA-1443000" - male
# cdpk4 - "PBANKA-0615200" - male

## create a list of landmark genes
list_of_landmark_genes <- c("PBANKA-1437500",
                            "PBANKA-0909600",
                            "PBANKA-1034300", 
                            "PBANKA-1319500", 
                            "PBANKA-0515000",
                            "PBANKA-0514900",
                            "PBANKA-1035200",
                            "PBANKA-0616700",
                            "PBANKA-1415700",
                            "PBANKA-1217700",
                            "PBANKA-0416100", 
                            "PBANKA-1212600",
                            "PBANKA-0933700",
                            "PBANKA-1443000",
                            "PBANKA-0615200")

name_of_landmark_genes <- c("AP2-G", 
                            "AP2_poran", 
                            "AP2-G2", 
                            "ccp2", 
                            "p25", 
                            "p28",
                            "ccp3",
                            "nek4",
                            "ap2-fg",
                            "DOZI",
                            "mg1", 
                            "hap2", 
                            "mapk2",
                            "nek1",
                            "cdpk4")

## make a df
name_of_landmark_genes <- data.frame("gene_name" = name_of_landmark_genes, "id" = list_of_landmark_genes)

## make dataframe
df_landmark_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% list_of_landmark_genes),]

## merge dataframes
df_landmark_gene_modules <- merge(df_landmark_gene_modules, name_of_landmark_genes, by = "id")

## inspect
df_landmark_gene_modules

enrichment of screen hits in modules

## read in screen hits
library("readxl")
screen_hits <- read_excel("../data/Screen/Modules_Clusters_Phenotypes.xlsx")

## get only hits
screen_hits_selected <- screen_hits[which(screen_hits$`Gam phenotype screen` == "Both" | screen_hits$`Gam phenotype screen` == "Females" | screen_hits$`Gam phenotype screen` == "Males"), ] 

## extract gene IDs
gene_hits <- screen_hits_selected$`new gene ID`
## change the underscore to a dash
gene_hits <- gsub("_", "-", gene_hits)

## find out which modules they are in
df_hits_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% gene_hits),]
print("screen hits by module")
table(df_hits_gene_modules$module)

## get the number genes screened in that module
screen_hits_screened <- screen_hits[which(screen_hits$`Gam phenotype screen` == "Both" | screen_hits$`Gam phenotype screen` == "Females" | screen_hits$`Gam phenotype screen` == "Males" | screen_hits$`Gam phenotype screen` == "None" | screen_hits$`Gam phenotype screen` == "male not enough power"), ] 
## extract gene IDs
genes_screened <- screen_hits_screened$`new gene ID`
## change the underscore to a dash
genes_screened <- gsub("_", "-", genes_screened)
## find out which modules they are in
df_screened_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% genes_screened),]
print("total genes screened in this module")
table(df_screened_gene_modules$module)

## make a table with this info
pc_screened <- data.frame(hits = table(df_hits_gene_modules$module), screened =  table(df_screened_gene_modules$module))[,-3]
names(pc_screened) <- c("module", "hits", "screened")
pc_screened$pc <- (pc_screened$hits /pc_screened$screened)*100

## view
pc_screened

Further investigation of screen hits

## rename df
df_screen_hits_selected <- as.data.frame(screen_hits_selected)
df_screen_hits_selected$'new gene ID' <- gsub("_", "-", df_screen_hits_selected$'new gene ID')
names(df_screen_hits_selected)[2] <- "id"
df_gene_module_df_sex <- as.data.frame(gene_module_df_sex)

## merge dfs together
screen_hits_modules <- merge(df_screen_hits_selected, df_gene_module_df_sex, by = "id")

## view
screen_hits_modules

DOZI-regulated genes

Find out how many of the genes in each module has a DOZI-regulated gene

DOZI_regulated_genes <-
read.csv(file = "../data/Reference/DOZI_regulated_genes.csv", header = TRUE)

## extract gene IDs
dozi_genes <- DOZI_regulated_genes[DOZI_regulated_genes$DOZI_regulated. == "YES", ]$Gene_ID_PB
## change the underscore to a dash
dozi_genes <- gsub("_", "-", dozi_genes)

## find out which modules they are in
df_dozi_gene_modules <- gene_module_df_sex[which(gene_module_df_sex$id %in% dozi_genes),]
print("dozi genes by module")
table(df_dozi_gene_modules$module)

Visualise module expression

plot out modules

## make aggregated df again so you can edit it
agg_mat <- aggregate_gene_expression(monocle.object, gene_module_df_sex, cell_group_df)

## h clust the aggregated matrix
module_dendro <- hclust(dist(agg_mat))

## use these clusters to reorder the modules
gene_module_df_sex$module <- factor(gene_module_df_sex$module, levels = row.names(agg_mat)[module_dendro$order])

## plot
UMAP_modules <- plot_cells(monocle.object, genes=gene_module_df_sex %>% filter(module %in% c(1:20)),
           cell_size = 2, 
           x = 2, y = 1,
           label_cell_groups=FALSE,
           scale_to_range = TRUE,
           show_trajectory_graph=FALSE) +
                          scale_colour_viridis_c(name = "expression", option = "C", alpha = 1) +
                          coord_fixed() +
                          theme_void() +
                          theme(legend.position = "bottom", strip.text.x = element_text(size = 15))

## view
UMAP_modules

save

ggsave("../images_to_export/GCSKO_sexbranch_umap_modules.png", plot = UMAP_modules, device = "png", path = NULL, scale = 1, width = 30, height = 30, units = "cm", dpi = 300, limitsize = TRUE)

## make a dataframe of genes per module
genes_per_module <- as.data.frame(table(gene_module_df_sex$module))

## inspect
genes_per_module

## A. Preparation of dataframe

## prepare custom dataframe for all cells by modules:
agg_mat_no_group <- aggregate_gene_expression(monocle.object, gene_module_df_sex, scale_agg_values = TRUE)

## B. annotations

## make an anotation
## add pt to the monocle object
monocle.object@colData$pt <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
## make an annotation dataframe
anno_no_group <- data.frame(monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, as.factor(monocle.object@colData$Prediction.Spearman._Kasia), row.names = rownames(monocle.object@colData))
names(anno_no_group) <- c("Sex", "Pseudotime", "Real_time_prediction")

## make annotation colours
annotation_colours <- list(Sex = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5"),
                           Pseudotime = plasma(12, direction = 1),
                           Real_time_prediction = c("0" = viridis(8, direction = 1)[1] ,"1" =  viridis(8, direction = 1)[2] ,"4" = viridis(8, direction = 1)[3] ,"6" = viridis(8, direction = 1)[4] ,"8" = viridis(8, direction = 1)[5]  ,"12"  = viridis(8, direction = 1)[6]  ,"18" = viridis(8, direction = 1)[7]  ,"24" = viridis(8, direction = 1)[8]))

## change the order of the cols (cells) in data frame
col.order <- rownames(anno_no_group[with(anno_no_group, order(Sex, Pseudotime)), ])
agg_mat_no_group <- agg_mat_no_group[,col.order]

## reorder the rows (gene modules) in the data frame so they are in pt order
## define the order visually and using the clusters originally produced
row.order <- c("8", "3","4", "15", "2", "18", "11",
               "7", "1", "13",
               "10", "14", "12", "16", "9",
               "5","6", "17",
               "19", "20")
## reorder using new order
agg_mat_no_group <- agg_mat_no_group[row.order, ]

## for cuts in columns - used later to count number of cells in each cat
df_meta <- as.data.frame(monocle.object@colData)
female_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Female"), ])
male_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Male"), ])
bi_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Bipotential"), ])
asex_cells <- rownames(df_meta[which(df_meta$cluster_colours_figure == "Asexual"), ])
rm(df_meta)

## add module and the number of cells to the row
## change names for row names to include "module " at the begining of them
labels.row <- stringr::str_c("Module ", row.names(agg_mat_no_group))
## reorder frequency so that it is matching our matrix
genes_per_module_ordered <- genes_per_module[match(row.names(agg_mat_no_group), genes_per_module$Var1), ]
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module_ordered$Freq)){
  labels.row[i] <- stringr::str_c(labels.row[i]," (n = " ,genes_per_module_ordered$Freq[i], ")")
}

## C. Plotting

## plot
heatmap_main <- pheatmap::pheatmap(agg_mat_no_group, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   ## others: ward.D2,
                   #clustering_method="complete",
                   show_colnames = FALSE,
                   labels_row = labels.row,
                   fontsize_row = 15,
                   fontsize = 15,
                   gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 6)

## view
heatmap_main

save

ggsave("../images_to_export/GCSKO_sexbranch_heatmap.png", plot = heatmap_main, device = "png", path = NULL, scale = 1, width = 27, height = 20, units = "cm", dpi = 300, limitsize = TRUE)

## A. Preparation of dataframe

## prepare custom dataframe for all cells by modules:
agg_mat_no_group <- aggregate_gene_expression(monocle.object, gene_module_df_sex, scale_agg_values = TRUE)

## B. annotations

## make an anotation
## add pt to the monocle object
monocle.object@colData$pt <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
## make an annotation dataframe
anno_no_group <- data.frame(monocle.object@colData$Sexes_monocle, monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, as.factor(monocle.object@colData$Prediction.Spearman._Kasia), row.names = rownames(monocle.object@colData))
## add names to this dataframe
names(anno_no_group) <- c("Sexes_monocle", "Sex", "Pseudotime", "Real_time_prediction")
## make the sexes classification a factor so it can be reordered (explaination here: https://rstudio-pubs-static.s3.amazonaws.com/7433_4537ea5073dc4162950abb715f513469.html)
anno_no_group$Sexes_monocle <- factor(anno_no_group$Sexes_monocle, levels = c("Asexual", "Unassigned", "Asexual_Fate", "Bipotential", "Female", "Male"))

## make annotation colours
annotation_colours <- list(Sexes_monocle = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5", Asexual_Fate = "#77A9D8", Unassigned = "#0052c5"),
                           Sex = c(Male="#016c00", Female="#a52b1e", Bipotential = "#ffe400", Asexual = "#0052c5"),
                           Pseudotime = plasma(12, direction = 1),
                           Real_time_prediction = c("0" = viridis(8, direction = 1)[1] ,"1" =  viridis(8, direction = 1)[2] ,"4" = viridis(8, direction = 1)[3] ,"6" = viridis(8, direction = 1)[4] ,"8" = viridis(8, direction = 1)[5]  ,"12"  = viridis(8, direction = 1)[6]  ,"18" = viridis(8, direction = 1)[7]  ,"24" = viridis(8, direction = 1)[8]))

## change the order of the cols (cells) in data frame
col.order <- rownames(anno_no_group[with(anno_no_group, order(Sexes_monocle, Pseudotime)), ])
agg_mat_no_group <- agg_mat_no_group[,col.order]

## reorder the rows (gene modules) in the data frame so they are in pt order
## define the order visually and using the clusters originally produced
row.order <- c("8", "3","4", "15", "2", "18", "11",
               "7", "1", "13",
               "10", "14", "12", "16", "9",
               "5","6", "17",
               "19", "20")
## reorder using new order
agg_mat_no_group <- agg_mat_no_group[row.order, ]

## for cuts in columns - used later to count number of cells in each cat
df_meta <- as.data.frame(monocle.object@colData)
female_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Female"), ])
male_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Male"), ])
bi_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Bipotential"), ])
asex_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Asexual"), ])
asex_fate_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Asexual_Fate"), ])
unassigned_cells <- rownames(df_meta[which(df_meta$Sexes_monocle == "Unassigned"), ])
rm(df_meta)

## add module and the number of cells to the row
## change names for row names to include "module " at the begining of them
labels.row <- stringr::str_c("Module ", row.names(agg_mat_no_group))
## reorder frequency so that it is matching our matrix
genes_per_module_ordered <- genes_per_module[match(row.names(agg_mat_no_group), genes_per_module$Var1), ]
## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module_ordered$Freq)){
  labels.row[i] <- stringr::str_c(labels.row[i]," (n = " ,genes_per_module_ordered$Freq[i], ")")
}

## C. Plotting

## plot
heatmap_main <- pheatmap::pheatmap(agg_mat_no_group, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = TRUE,
                   ## others: ward.D2,
                   #clustering_method="complete",
                   show_colnames = FALSE,
                   labels_row = labels.row,
                   fontsize_row = 15,
                   fontsize = 15,
                   gaps_col = c(length(asex_cells), length(c(asex_cells,unassigned_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells,bi_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells,bi_cells, female_cells)), length(c(asex_cells,unassigned_cells, asex_fate_cells,bi_cells, female_cells, male_cells))),
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 6)

## view
heatmap_main

This is for:

## repeat of plot above:
## Plot
umap_pt <- plot_cells(monocle.object, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 2, y = 1, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
  coord_fixed() +
  theme_void() +
  labs(title = "Pseudotime") +
  theme(plot.title = element_text(hjust = 0.5))

## view
umap_pt

Visualise the expression of select genes over pseudotime

## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
#list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

## define genes to plot
list_of_genes_of_interest <- c("PBANKA-0102400", "PBANKA-0413400", "PBANKA-0716500", "PBANKA-0828000", "PBANKA-0902300", "PBANKA-1144800", "PBANKA-1302700", "PBANKA-1418100", "PBANKA-1435200", "PBANKA-1447900", "PBANKA-1454800", "PBANKA-1437500", "PBANKA-1319500", "PBANKA-0416100", "PBANKA-1034300")
## add names
names_of_genes_of_interest <- c("GCSKO-2", "GCSKO-10", "GCSKO-19", "GCSKO-3", "GCSKO-13", "GCSKO-28", "GCSKO-oom", "GCSKO-17", "GCSKO-20", "GCSKO-29", "GCSKO-21", "AP2-G", "CCP2", "MG1", "AP2-G2")

##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)), name = names_of_genes_of_interest)
## reorder
#genes_of_interest <- genes_of_interest[match(c("AP2-G", "CCP2", "GCSKO-21", "GCSKO-17", "GCSKO-2", "MG1", "GCSKO-20", "GCSKO-3", "GCSKO-oom", "GCSKO-29", "GCSKO-10", "GCSKO-28", "GCSKO-19", "GCSKO-13"), genes_of_interest$name), ]

## prepare custom dataframe for all cells by modules:
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest[,1:2])

## reorder using new order
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,col.order]

## plot
heatmap_plot <- pheatmap::pheatmap(agg_mat_genes_of_interest, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = TRUE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   labels_row = as.character(genes_of_interest[,3]),
                   gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
                   #gaps_row = c(1, 6),
                   cutree_rows = 2,
                   ## trying to fix legend issue here
                   #fontsize_row = 10,
                   #fontsize_col = 3,
                   #cellheight=3, 
                   #cellwidth = 3,
                   legend = TRUE,
                   annotation_legend = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours
                   )

heatmap_plot

look at some of the “module” genes now too

## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
#list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

## define genes to plot
list_of_genes_of_interest <- c("PBANKA-1454000", "PBANKA-1354000")
## add names
names_of_genes_of_interest <- c("GyrB", "DBR1")

##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)), name = names_of_genes_of_interest)
## reorder
#genes_of_interest <- genes_of_interest[match(c("AP2-G", "CCP2", "GCSKO-21", "GCSKO-17", "GCSKO-2", "MG1", "GCSKO-20", "GCSKO-3", "GCSKO-oom", "GCSKO-29", "GCSKO-10", "GCSKO-28", "GCSKO-19", "GCSKO-13"), genes_of_interest$name), ]

## prepare custom dataframe for all cells by modules:
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest[,1:2])

## reorder using new order
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,col.order]

## plot
heatmap_plot <- pheatmap::pheatmap(agg_mat_genes_of_interest, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = TRUE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   labels_row = as.character(genes_of_interest[,3]),
                   gaps_col = c(length(asex_cells), length(c(asex_cells,bi_cells)), length(c(asex_cells,bi_cells,female_cells))),
                   #gaps_row = c(1, 6),
                   cutree_rows = 2,
                   ## trying to fix legend issue here
                   #fontsize_row = 10,
                   #fontsize_col = 3,
                   #cellheight=3, 
                   #cellwidth = 3,
                   legend = TRUE,
                   annotation_legend = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours
                   )

heatmap_plot

Plot specific genes of interest

Side plots

construct new dataframes for the cells from mutants for each sex

## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly

## male
## subset out only male and pre determination cells
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$at_sex == "male"), ]
## take forward only smart-seq2
male_cells <- male_cells[which(male_cells$experiment == "mutants"), ]
## get cell names
male_cells <- rownames(male_cells)
## subset Seurat object to contain cells of interest  
male.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = male_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(male.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(male.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
male.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
male.monocle.object = preprocess_cds(male.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
#male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))  
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, exclude.na = FALSE)

## female
## subset out only male and pre determination cells
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$at_sex == "female"), ]
## take forward only wild-type
female_cells <- female_cells[which(female_cells$experiment == "mutants"), ]
## get cell names
female_cells <- rownames(female_cells)
## subset our cell group df to keep only these cells
#female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## subset Seurat object to contain cells of interest  
female.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = female_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(female.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(female.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
female.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
female.monocle.object = preprocess_cds(female.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
#female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))  
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, exclude.na = FALSE)

male

# male_agg_mat
## reorder using new order
male_agg_mat <- male_agg_mat[row.order, ]

## make an anotation
anno_male <- data.frame(male.monocle.object@colData$at_sex, male.monocle.object@colData$old_pt_values, genotype = male.monocle.object@colData$identity_updated, row.names = rownames(male.monocle.object@colData))
names(anno_male) <- c("sex", "Pseudotime", "genotype")

## make annotation colours
annotation_colours <- list(sex = c(male="#016c00", female="#a52b1e", 'pre-det' = "#0052c5"),
                           Pseudotime = magma(12, direction = 1))

## change the order of the data frame
col.order.male <- rownames(anno_male[with(anno_male, order(genotype, Pseudotime)), ])
male_agg_mat <- male_agg_mat[,col.order.male]

## plot
heatmap_male <- pheatmap::pheatmap(male_agg_mat, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   legend = FALSE,
                   annotation_legend = TRUE,
                   annotation_col = anno_male, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 12)

heatmap_male

# female_agg_mat
## reorder using new order
female_agg_mat <- female_agg_mat[row.order, ]

## make an anotation
anno_female <- data.frame(female.monocle.object@colData$at_sex, female.monocle.object@colData$old_pt_values, genotype = female.monocle.object@colData$identity_updated, row.names = rownames(female.monocle.object@colData))
names(anno_female) <- c("sex", "Pseudotime", "genotype")

## make annotation colours
annotation_colours <- list(sex = c(male="#016c00", female="#a52b1e", 'pre-det' = "#0052c5"),
                           Pseudotime = magma(12, direction = 1))

## change the order of the data frame
col.order.female <- rownames(anno_female[with(anno_female, order(genotype, Pseudotime)), ])
female_agg_mat <- female_agg_mat[,col.order.female]

## plot
heatmap_female <- pheatmap::pheatmap(female_agg_mat, 
                   #scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   legend = FALSE,
                   annotation_legend = FALSE,
                   annotation_col = anno_female, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 12)

heatmap_female

## save a pheatmap: https://stackoverflow.com/questions/43051525/how-to-draw-pheatmap-plot-to-screen-and-also-save-to-file

side plots with groups of mutant cells

female

## make a new grouping for cells based on their identity
mutant_group_female <- data.frame(cell = rownames(female.monocle.object@colData), cell_group = female.monocle.object@colData$identity_updated)

## aggregate expression
female_agg_mat_grouped <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, mutant_group_female, exclude.na = FALSE)

## reorder using new order
female_agg_mat_grouped <- female_agg_mat_grouped[row.order, ]

## plot
pheatmap::pheatmap(female_agg_mat_grouped, 
                   #scale="row",
                   cluster_cols = TRUE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = TRUE,
                   legend = FALSE,
                   annotation_legend = FALSE,
                   #annotation_col = anno_female, 
                   #annotation_colors = annotation_colours, 
                   cutree_rows = 12)

male

module 12 inspection

## make a df for module 12 genes
module.12.genes <- gene_module_df_sex[gene_module_df_sex$module == 12, ]$id
module.12.genes <- data.frame(id = module.12.genes, group = module.12.genes)

## prepare custom dataframe for all cells by modules:
agg_mat_module_12 <- aggregate_gene_expression(monocle.object, module.12.genes)

## make an anotation
anno_no_group <- data.frame(monocle.object@colData$sex_UMAP, monocle.object@colData$old_pt_values, row.names = rownames(monocle.object@colData))
names(anno_no_group) <- c("sex", "Pseudotime")

## make annotation colours
annotation_colours <- list(sex = c(male="#016c00", female="#a52b1e", 'pre-det' = "#0052c5"),
                           Pseudotime = magma(12, direction = 1))

## change the order of the data frame
col.order <- c(pre_det_ordered_cells, female_ordered_cells, male_ordered_cells)
agg_mat_module_12 <- agg_mat_module_12[,col.order]

## plot
pheatmap::pheatmap(agg_mat_module_12, 
                   #scale="row",
                   cluster_cols = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours,
                   fontsize_row = 7,
                   cutree_rows = 12)

save plot

heatmap_module_12 <- pheatmap::pheatmap(agg_mat_module_12, 
                   #scale="row",
                   cluster_cols = FALSE,
                   clustering_method="ward.D2",
                   show_colnames = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours,
                   fontsize_row = 7,
                   cutree_rows = 12)

AP2 Expression

## reading table of AP2 genes
ap2_genes_table <- read.delim(file = "~/data/AP2_genes_table.txt", header = TRUE, sep ="\t")

## extract list of genes
ap2_genes_list <- dplyr::pull(ap2_genes_table, Gene.ID)
ap2_genes_list <- gsub("_", "-", ap2_genes_list)

## make a df for genes
ap2_genes_list <- data.frame(id = ap2_genes_list, group = ap2_genes_list)

## prepare custom dataframe for all cells by modules:
agg_mat_ap2s <- aggregate_gene_expression(monocle.object, ap2_genes_list)

## change the order of the data frame
col.order <- c(pre_det_ordered_cells, female_ordered_cells, male_ordered_cells)
agg_mat_ap2s <- agg_mat_ap2s[,col.order]

## plot
pheatmap::pheatmap(agg_mat_ap2s, 
                   #scale="row",
                   cluster_cols = FALSE,
                   clustering_method="complete",
                   show_colnames = FALSE,
                   annotation_col = anno_no_group, 
                   annotation_colors = annotation_colours,
                   fontsize_row = 7,
                   cutree_rows = 3)

Module Analysis

Read in Kasia’s modules:

## read in kasia modules:
kasia_clusters <- read.csv(file = "~/data/Modules_Clusters_Phenotypes.csv", header = TRUE)

## change _ to -:
kasia_clusters$new.gene.ID <- gsub("_", "-", kasia_clusters$new.gene.ID)

## filter out genes not in modules gene_module_df_sex:
kasia_clusters_filtered <- kasia_clusters[which(kasia_clusters$new.gene.ID %in% gene_module_df_sex$id), ]

## rename new gene id
names(kasia_clusters_filtered)[2] <- "id"

## merge together
modules_merged_df <- merge(kasia_clusters_filtered, gene_module_df_sex, by = "id")

## look at the enrichment with a dotplot:
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(modules_merged_df$Kasia.Cluster, df_meta_data$identity_combined), margin = 2)) * 100)

NOT USED complex heatmap version

## make into matrix to plot
col.order <- agg_mat_all_cells_matrix <- as.matrix(agg_mat_all_cells)

make annotation

## extract pseudotime values:
pt_values <- as.data.frame(pseudotime(monocle.object, reduction_method = "UMAP"))
names(pt_values) <- "monocle_pt_sex_wt"
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, pt_values)

## save pt values
write.csv(pt_values, file = "~/data_to_export/pt_values_sex_only.csv")

library(circlize)
## make the annotation df
## get meta data from seurat object and then subset rows out that are wt
df_anno <- tenx.mutant.integrated.sex@meta.data[which(rownames(tenx.mutant.integrated.sex@meta.data) %in% colnames(agg_mat_all_cells_matrix)), ]
## get only columns of interest:
df_anno <- df_anno[ ,c("sex", "monocle_pt_sex_wt"), drop = FALSE ]

## order annotation
df_anno <- df_anno[with(df_anno, order(sex, monocle_pt_sex_wt)),]

## order cols in the matrix
agg_mat_all_cells_matrix <- agg_mat_all_cells_matrix[ ,match(colnames(agg_mat_all_cells_matrix), rownames(df_anno))]

## make annotation
heatmap_annotation <- HeatmapAnnotation(df = cluster_anno, 
                                        col = list(sex = c(male="#016c00", female="#a52b1e", `pre-det` = "#0052c5"), monocle_pt_sex_wt = colorRamp2(c(1:70), inferno(70)))
                                        )

heatmap_annotation <- HeatmapAnnotation(sex = df_anno$sex, pt = df_anno$monocle_pt_sex_wt)

plot

## make heatmap
modules_heatmap <- Heatmap(agg_mat_all_cells_matrix,
        column_order = NULL,
        cluster_columns = FALSE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

## extract counts from 10x object
matrix_tenx_counts <- as.matrix(GetAssayData(pb_sex_filtered, assay = "RNA"))
#nk.raw.data <- as.matrix(GetAssayData(pb_sex_filtered, slot = "counts")[, WhichCells(pbmc, ident = "NK")])

## check it is the same as the merged object RNA slot

## check it is the same as the monocle object
matrix_tenx_counts_monocle <- as.matrix(as.data.frame((monocle.object@assays)))

## make heatmap
modules_heatmap <- Heatmap(matrix_tenx_counts,
        column_order = NULL,
        cluster_columns = TRUE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(matrix_tenx_counts)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        #bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

plot as a function of pseudotime

Now, we will integrate the branch data we produced using slingshot and the pseudotime values to plot this heatmap.

Monocle3 has a handy function that allows us to aggregate expression of groups of cells called aggregate_gene_expression.

The code for this is located here: https://github.com/cole-trapnell-lab/monocle3/blob/1a02274209c765fe7a60f533a31b1da3dacf6785/R/cluster_genes.R

Define the groups of cells

## Split cells into groups of sexes
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "female"), ]
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "male"), ]
pre_det_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det"), ]

## inspect range of pt values to determine bin width
hist(female_cells$PT_LineageFemale)
hist(male_cells$PT_LineageMale)
hist(pre_det_cells$PT_LineageFemale)
hist(pre_det_cells$PT_LineageMale)

Use a bin width of 2

there will be two objects for the cell_group_df: male branch and female branch. Both will include the pre-det branch

## Define male and female branch cells
# male
male_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "male"), ]

male_branch_meta_data <- data.frame(cell_id = rownames(male_branch_meta_data), pt = male_branch_meta_data$PT_LineageMale)

male_cell_group_df <- male_branch_meta_data

#female
female_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "female"), ]

female_branch_meta_data <- data.frame(cell_id = rownames(female_branch_meta_data), pt = female_branch_meta_data$PT_LineageFemale)

female_cell_group_df <- female_branch_meta_data

## what's the range of values for each pt?

range(female_cell_group_df$pt)
range(male_cell_group_df$pt)

## make bin widths
# make a new col for annotation
female_cell_group_df$pt_bin <- NA
for(i in seq(2,68,2)){
  female_cell_group_df$pt_bin[which(female_cell_group_df$pt < i & female_cell_group_df$pt >= (i-2))] <- i
}

male_cell_group_df$pt_bin <- NA
for(i in seq(2,68,2)){
  male_cell_group_df$pt_bin[which(male_cell_group_df$pt < i & male_cell_group_df$pt >= (i-2))] <- i
}
# then remove old pt values
male_cell_group_df <- male_cell_group_df[ ,-2]
female_cell_group_df <- female_cell_group_df[ ,-2]

## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly

## male
## subset out only male and pre determination cells
male_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "male" | tenx.mutant.integrated.sex@meta.data$sex == "pre-det"), ]
## take forward only wild-type
male_cells <- male_cells[which(male_cells$identity_combined == "WT" | male_cells$identity_combined == "WT_10X"), ]
#male_cells <- male_cells[which(male_cells$identity_combined == "WT_10X"), ]
## get cell names
male_cells <- rownames(male_cells)
## subset our cell group df to keep only these cells
male_cell_group_df <- male_cell_group_df[which(male_cell_group_df$cell_id %in% male_cells),]
## subset Seurat object to contain cells of interest  
male.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = male_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(male.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(male.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
male.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
male.monocle.object = preprocess_cds(male.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))  
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, male_cell_group_df, exclude.na = FALSE)

## female
## subset out only male and pre determination cells
female_cells <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "female" | tenx.mutant.integrated.sex@meta.data$sex == "pre-det"), ]
## take forward only wild-type
female_cells <- female_cells[which(female_cells$identity_combined == "WT" | female_cells$identity_combined == "WT_10X"), ]
#female_cells <- female_cells[which(female_cells$identity_combined == "WT_10X"), ]
## get cell names
female_cells <- rownames(female_cells)
## subset our cell group df to keep only these cells
female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## subset Seurat object to contain cells of interest  
female.seurat.object <- SubsetData(tenx.mutant.integrated.sex, cells = female_cells)
## make new counts and pheno:
data <- as(as.matrix(GetAssayData(female.seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(female.seurat.object@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
female.monocle.object <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
female.monocle.object = preprocess_cds(female.monocle.object, num_dim = 50, norm_method = "none")  
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))  
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, female_cell_group_df, exclude.na = FALSE)

## use these clusters to reorder the modules
male_agg_mat <- male_agg_mat[match(levels(gene_module_df_sex$module), row.names(male_agg_mat)), ]
female_agg_mat <- female_agg_mat[match(levels(gene_module_df_sex$module), row.names(female_agg_mat)), ]

pheatmap::pheatmap(male_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(male_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(male_agg_mat, 
                   scale="none",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="none",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

ComplexHeatmap version

## pheatmap calculates Z scores for plotting values
scale_matrix_by_cols <- function (x) 
{
    m = apply(x, 1, mean, na.rm = T)
    s = apply(x, 1, sd, na.rm = T)
    return((x - m)/s)
}

## calculate z score by col
female_agg_mat_scaled <- t(as.matrix(scale_matrix_by_cols(t(female_agg_mat))))
male_agg_mat_scaled <- t(as.matrix(scale_matrix_by_cols(t(male_agg_mat))))
## reorder cols
female_agg_mat_scaled <- female_agg_mat_scaled[match(levels(gene_module_df_sex$module), row.names(female_agg_mat_scaled)), ]
male_agg_mat_scaled <- male_agg_mat_scaled[match(levels(gene_module_df_sex$module), row.names(male_agg_mat_scaled)), ]

## reorder based on clusters
genes_per_module <- genes_per_module[match(levels(gene_module_df_sex$module), row.names(genes_per_module)), ]

## change names for row names to include "module " at the begining of them
row.names(female_agg_mat_scaled) <- stringr::str_c("Module ", row.names(female_agg_mat_scaled))
row.names(male_agg_mat_scaled) <- stringr::str_c("Module ", row.names(male_agg_mat_scaled))

## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module$Freq)){
  row.names(female_agg_mat_scaled)[i] <- stringr::str_c(row.names(female_agg_mat_scaled)[i]," (n = " ,genes_per_module$Freq[i], ")")
}
for(i in seq_along(genes_per_module$Freq)){
  row.names(male_agg_mat_scaled)[i] <- stringr::str_c(row.names(male_agg_mat_scaled)[i]," (n = " ,genes_per_module$Freq[i], ")")
}

## add annotation:
#heatmap_annotation <- HeatmapAnnotation(df = cluster_anno,
#                                         col = list(
# Identity = c(Male="#016c00", Female="#a52b1e", Asexual= "#0052c5", Committed = "#f2eb23")),
#                                         annotation_legend_param = list(Median_Pseudotime_of_Cluster = list(direction = "horizontal"), Identity = list(nrow = 1)))

library(ComplexHeatmap)
library(RColorBrewer)
modules_heatmap_female <- Heatmap(female_agg_mat_scaled,
        column_order = NULL,
        #row_order = row.names(female_agg_mat_scaled)[module_dendro$order],
        #clustering_method_rows = "ward.D2",
        #bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

modules_heatmap_male <- Heatmap(male_agg_mat_scaled,
        column_order = NULL,
        #row_order = module_dendro$order,
        #clustering_method_rows = "ward.D2",
        #bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

draw(modules_heatmap_female, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")
draw(modules_heatmap_male, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

## https://www.biostars.org/p/380544/ 

4 bin width

## Define male and female branch cells
# male
male_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "male"), ]

male_branch_meta_data <- data.frame(cell_id = rownames(male_branch_meta_data), pt = male_branch_meta_data$PT_LineageMale)

male_cell_group_df <- male_branch_meta_data

#female
female_branch_meta_data <- tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "female"), ]

female_branch_meta_data <- data.frame(cell_id = rownames(female_branch_meta_data), pt = female_branch_meta_data$PT_LineageFemale)

female_cell_group_df <- female_branch_meta_data

## what's the range of values for each pt?

range(female_cell_group_df$pt)
range(male_cell_group_df$pt)

## make bin widths
# make a new col for annotation
female_cell_group_df$pt_bin <- NA
for(i in seq(4,68,4)){
  female_cell_group_df$pt_bin[which(female_cell_group_df$pt < i & female_cell_group_df$pt >= (i-4))] <- i
}

male_cell_group_df$pt_bin <- NA
for(i in seq(4,68,4)){
  male_cell_group_df$pt_bin[which(male_cell_group_df$pt < i & male_cell_group_df$pt >= (i-4))] <- i
}
# then remove old pt values
male_cell_group_df <- male_cell_group_df[ ,-2]
female_cell_group_df <- female_cell_group_df[ ,-2]

## The original object contains all cells, we just want wild-type so let's subset out gene_module_df and cell_group_df accordingly

## male
## subset our cell group df to keep only these cells
male_cell_group_df <- male_cell_group_df[which(male_cell_group_df$cell_id %in% male_cells),]
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
male_cell_group_df <- data.frame(cell=as.character(factor(male_cell_group_df$cell_id)), cell_group=factor(male_cell_group_df$pt_bin))  
## aggregate expression
male_agg_mat <- aggregate_gene_expression(male.monocle.object, gene_module_df_sex, male_cell_group_df, exclude.na = FALSE)

## female
## subset out only male and pre determination cells
## subset our cell group df to keep only these cells
female_cell_group_df <- female_cell_group_df[which(female_cell_group_df$cell_id %in% female_cells),]
## make a new dataframe for cell groups - it is crucial to refactor otherwise aggregate_gene_expression thinks it's out of bounds  
female_cell_group_df <- data.frame(cell=as.character(factor(female_cell_group_df$cell_id)), cell_group=factor(female_cell_group_df$pt_bin))  
## aggregate expression
female_agg_mat <- aggregate_gene_expression(female.monocle.object, gene_module_df_sex, female_cell_group_df, exclude.na = FALSE)

## use these clusters to reorder the modules
male_agg_mat <- male_agg_mat[match(levels(gene_module_df_sex$module), row.names(male_agg_mat)), ]
female_agg_mat <- female_agg_mat[match(levels(gene_module_df_sex$module), row.names(female_agg_mat)), ]

pheatmap::pheatmap(male_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

pheatmap::pheatmap(female_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE)

expression of modules in mutant cells (side panels)

male

## make monocle object with mutants
## extract data
mutant_cells_male <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == "Mutant" & tenx.mutant.integrated.sex@meta.data$sex == "male"),])

## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = mutant_cells_male)

## make new counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(seurat.object@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object.mutants.male <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)

## preprocess
monocle.object.mutants.male = preprocess_cds(monocle.object.mutants.male, num_dim = 50, norm_method = "none")
### if using integrated data:
# norm_method = "none", alignment_group = "~ experiment"

## make a cell group dataframe for aggregating expression values:

mutant_cell_group_df <- data.frame(cell = row.names(monocle.object.mutants.male@colData), cell_group = monocle.object.mutants.male@colData$identity_updated)

## aggregate expression
mutant_male_agg_mat <- aggregate_gene_expression(monocle.object.mutants.male, gene_module_df_sex, mutant_cell_group_df)

plot

mutant_male_agg_mat <- mutant_male_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_male_agg_mat)), ]

pheatmap::pheatmap(mutant_male_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = TRUE) + theme(legend.position = "bottom")

mutant_male_agg_mat <- mutant_male_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_male_agg_mat)), ]

pheatmap::pheatmap(mutant_male_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = TRUE) + theme(legend.position = "bottom")

female

## make monocle object with mutants
## extract data
mutant_cells_female <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$genotype_combined == "Mutant" & tenx.mutant.integrated.sex@meta.data$sex == "female"),])

## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = mutant_cells_female)

## make new counts and pheno:
## the reason we use the integrated and then subsetted is because these cells have been normalised whereas the cells in pb_sex_filtered have not been normalised (well they have but with doublets in them)
data <- as(as.matrix(GetAssayData(seurat.object, assay = "RNA", slot = "data")), 'sparseMatrix')
pd <- data.frame(seurat.object@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))

## Construct monocle cds
monocle.object.mutants.female <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)

## preprocess
monocle.object.mutants.female = preprocess_cds(monocle.object.mutants.female, num_dim = 50, norm_method = "none")
### if using integrated data:
# norm_method = "none", alignment_group = "~ experiment"

## make a cell group dataframe for aggregating expression values:
mutant_cell_group_df <- data.frame(cell = row.names(monocle.object.mutants.female@colData), cell_group = monocle.object.mutants.female@colData$identity_updated)

## aggregate expression
mutant_female_agg_mat <- aggregate_gene_expression(monocle.object.mutants.female, gene_module_df_sex, mutant_cell_group_df)

plot

mutant_female_agg_mat <- mutant_female_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_female_agg_mat)), ]

pheatmap::pheatmap(mutant_female_agg_mat, 
                   scale="column",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = FALSE) + theme(legend.position = "bottom")

mutant_female_agg_mat <- mutant_female_agg_mat[match(levels(gene_module_df_sex$module), row.names(mutant_female_agg_mat)), ]

pheatmap::pheatmap(mutant_female_agg_mat, 
                   scale="row",
                   #clustering_method="ward.D2",
                   cluster_rows = FALSE,
                   cluster_cols = TRUE) + theme(legend.position = "bottom")

for particular genes (lower panel)

## landmark genes (genes of interest)
# AP2G - PBANKA-1437500
# AP2 - PBANKA-0909600 - from poran paper
# AP2G-2 - PBANKA-1034300 
list_of_mutant_genes <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")

list_of_genes_of_interest <- c("PBANKA-1437500", "PBANKA-0909600","PBANKA-1034300", "PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-1435200", "PBANKA-1418100", "PBANKA-1144800", "PBANKA-0902300", "PBANKA-0413400", "PBANKA-1454800")
##make df for genes of interest
genes_of_interest <- data.frame(gene = list_of_genes_of_interest, group = c(1:length(list_of_genes_of_interest)))

## aggregate expression
## make plotting df
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, cell_group_df)

row.names(agg_mat_genes_of_interest) <- genes_of_interest$gene

#row.names(agg_mat_genes_of_interest) <- factor(row.names(agg_mat_genes_of_interest), levels = row.names(agg_mat_genes_of_interest)[module_dendro$order])
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[,match(rownames(cluster_anno), colnames(agg_mat_genes_of_interest))]

pheatmap::pheatmap(agg_mat_genes_of_interest, 
                   scale="row",
                   cluster_cols = FALSE,
                   cluster_rows = FALSE,
                   clustering_method="ward.D2", 
                   annotation_col = cluster_anno, 
                   annotation_colors = annotation_colours)

complex heat map

## aggregate gene expression
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, df_all_cells)

agg_mat_genes_of_interest <- as.matrix(agg_mat_genes_of_interest)

## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
        column_order = NULL,
        cluster_columns = FALSE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

## order cols in the matrix
agg_mat_genes_of_interest <- agg_mat_genes_of_interest[ ,match(rownames(df_anno), colnames(agg_mat_genes_of_interest))]

## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
        column_order = NULL,
        cluster_columns = TRUE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

Using Seurat to visualise cells

# find markers for every cluster compared to all remaining cells, report only the positive ones
tenx.mutant.integrated.sex.markers <- FindAllMarkers(tenx.mutant.integrated.sex, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
tenx.mutant.integrated.sex.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)

top10 <- tenx.mutant.integrated.sex.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(tenx.mutant.integrated.sex, features = top10$gene) + NoLegend()

But we also have the old pt values that we can use in the seurat object ie FeaturePlot(tenx.mutant.integrated.sex, reduction = “pca”, pt.size = 0.01, features = “old_pt_values”)

So let’s plot a heatmap where we plot: (x) all cells vs. (y) genes arranged by module that they belong to.

add an old pt annotation to the top

prepare data:

## extracts only 10x cells 
wt_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"),])

## make a new Seurat of this
seurat.object <-SubsetData(tenx.mutant.integrated.sex, cells = wt_cells)

DoHeatmap(seurat.object, features = top10$gene) + NoLegend()

## aggregate gene expression
agg_mat_genes_of_interest <- aggregate_gene_expression(monocle.object, genes_of_interest, df_all_cells)

agg_mat_genes_of_interest <- as.matrix(agg_mat_genes_of_interest)

## make heatmap
modules_heatmap <- Heatmap(agg_mat_genes_of_interest,
        column_order = NULL,
        cluster_columns = FALSE,
        cluster_rows = FALSE,
        show_column_dend = FALSE,
        column_labels = rep("", ncol(agg_mat_all_cells_matrix)),
        #row_order = module_dendro$order,
        clustering_method_columns = "ward.D2",
        bottom_annotation = heatmap_annotation,
        col = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100), 
        heatmap_legend_param = list(direction = "horizontal"))

## print
draw(modules_heatmap, merge_legend = TRUE, heatmap_legend_side = "bottom", 
    annotation_legend_side = "bottom")

Expression of CCP2 and MG1 by each genotype and each sex

# ccp2 - "PBANKA-1319500" - female 820
# MG1 - "PBANKA-0416100" - male 820

## make a custom dataframe:
marker_820_df <- as.data.frame(t(as.data.frame(tenx.mutant.integrated.sex@assays$RNA@data[c("PBANKA-1319500", "PBANKA-0416100"), ], stringsAsFactors=F)))
marker_820_df$cell_id <- row.names(marker_820_df)
sex_id <- data.frame(sex_at = tenx.mutant.integrated.sex@meta.data$at_sex, genotype = tenx.mutant.integrated.sex@meta.data$identity_updated ,cell_id = row.names(tenx.mutant.integrated.sex@meta.data))
marker_820_df <- merge(marker_820_df, sex_id, by = "cell_id")

ggplot(marker_820_df, aes(fill=sex_at, y=`PBANKA-1319500`, x=genotype)) + 
    geom_violin() +
  geom_jitter(shape=16, position=position_jitter(0.2)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90))

#tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$sex == "pre-det" | tenx.mutant.integrated.sex@meta.data$sex == "female"), ]


VlnPlot(tenx.mutant.integrated.sex, group.by = "identity_updated", split.by = "at_sex", features = c("PBANKA-1319500"), split.plot = TRUE)

VlnPlot(tenx.mutant.integrated.sex, group.by = "identity_updated", split.by = "at_sex", features = c("PBANKA-0416100"), split.plot = TRUE)

Differential expression

Re-cluster the data so we have very course grain clusters

## find new clusters
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 1, random.seed = 42, algorithm = 2)

## plot the graph
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.1") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

VlnPlot(tenx.mutant.integrated.sex, features = c("PT_Female_UMAP", "PT_Male_UMAP"))

Show representation of genotypes per cluster

prep for dotplot

## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)

## define order for plotting 
my_levels_sex <- c("2", "3", "9", "6", "11", "5", "0", "10", "13", "14", "12", "8", "15", "1", "4", "7")

## redefine order of clusters:
df_meta_data$seurat_clusters <- factor(x = df_meta_data$seurat_clusters, levels = my_levels_sex)

## make a new df of CLUSTER and IDENTITY
dot_plot_df <- as.data.frame.matrix(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined))
dot_plot_df$cluster <- rownames(dot_plot_df)

## calculate percentage of cells for each genotype
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined), margin = 2)) * 100)

## make a column for cluster names
dot_plot_df_pc$cluster <- rownames(dot_plot_df_pc)

## melt dataframe for plotting
library(reshape2)
dot_plot_df_pc_melted <- melt(dot_plot_df_pc, variable.name = "cluster")
colnames(dot_plot_df_pc_melted)[2] <- "identity"

## melt the raw number too
dot_plot_df_melted <- melt(dot_plot_df, variable.name = "cluster")
colnames(dot_plot_df_melted)[2] <- "identity"
colnames(dot_plot_df_melted)[3] <- "raw_number"

## merge together
identical(dot_plot_df_melted$cluster, dot_plot_df_pc_melted$cluster)
dot_plot_merged <- cbind(dot_plot_df_melted, dot_plot_df_pc_melted)
dot_plot_merged <- dot_plot_merged[,c(1,2,3,6)]

## redefine order of clusters
dot_plot_merged$cluster <- factor(x = dot_plot_merged$cluster, levels = my_levels_sex)

## where values are zero, add NA
## find wells where it's zero
zero_values <- dot_plot_merged$value == 0
dot_plot_merged$value[zero_values] <- NA

## also do for raw number
zero_values <- dot_plot_merged$raw_number == 0
dot_plot_merged$raw_number[zero_values] <- NA

## reorder x axis:
my_levels_genotype <- c("GCSKO-oom", "GCSKO-29", "GCSKO-2", "GCSKO-19", "GCSKO-3", "GCSKO-21", "GCSKO-13", "GCSKO-28", "GCSKO-10_820", "GCSKO-17", "GCSKO-20", "WT", "WT_10X")

dot_plot_merged$identity <- factor(x = dot_plot_merged$identity, levels = my_levels_genotype)

plot

## plot
dot_plot_identity <- ggplot(dot_plot_merged, aes(y = factor(cluster), x = factor(identity))) +
      ## make into a dot plot
      geom_point(aes(colour=value, size=raw_number)) + 
      scale_color_gradient(low="blue", high="red", limits=c( 0, max(dot_plot_df_pc_melted$value)), na.value="white") +
      #change the colours
      scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white") +
      theme_classic() +
      theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), text=element_text(size=16,  family="Arial")) +
      ylab("Cluster") +
      xlab("Identity") +
      theme(axis.text.x=element_text(size=12, angle=45, hjust=1, vjust=1), axis.text.y=element_text(size=12,), legend.position = "bottom", plot.margin = unit(c(1,3,1,3), "lines")) +
    ## annotate males
    geom_hline(aes(yintercept = 2.5)) +
    ## annotate females
    geom_hline(aes(yintercept = 7.5)) +
    ## annotate pheno 1
    geom_vline(aes(xintercept = 4.5)) +
    ## annotate pheno 2
    geom_vline(aes(xintercept = 5.5)) +    
    ## annotate pheno 3
    geom_vline(aes(xintercept = 7.5)) +
    ## annotate pheno 4
    geom_vline(aes(xintercept = 11.5))

## print
print(dot_plot_identity)

cut off and plot

pre_branch_cells <- c(2,3)
inmature_male_cells <- c()
inmature_female_cells <-
mature_male_cells <- 
mature_female_cells <- 

## plot the graph
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.1") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom")

wt_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT" | tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"), ])

male_inmature_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT" | tenx.mutant.integrated.sex@meta.data$identity_combined == "WT_10X"), ])



early_wt_cells <- row.names(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$identity_combined == "WT" && tenx.mutant.integrated.sex@meta.data$PT_LineageFemale < 46), ])

FindMarkers(tenx.mutant.integrated.sex, cells.1 = , cells.2 = )

Generate New Clusters

We must now recalculate the clusters to gain a better understanding of the heterogeneity of the subset. Since using the previous clusters, the asexual cells obscured some of the variation.

## copy old clusters
tenx.mutant.integrated.sex <- AddMetaData(tenx.mutant.integrated.sex, tenx.mutant.integrated.sex@meta.data$seurat_clusters, col.name = "pre_sex_clusters")

## generate new clusters at various resolutions
tenx.mutant.integrated.sex <- FindNeighbors(tenx.mutant.integrated.sex, dims = 1:15)
tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = c(2,3,4,5,6), random.seed = 42, algorithm = 2)

Visualise

Choose Cluster Resolution

View the clusters at different resolutions to chose the appropraite resolution

## plot
DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.2") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.3") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.4") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.5") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5, group.by = "integrated_snn_res.6") +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

Go with 4 clusters

tenx.mutant.integrated.sex <- FindClusters(tenx.mutant.integrated.sex, resolution = 4, random.seed = 42, algorithm = 2)

DimPlot(tenx.mutant.integrated.sex, reduction = "umapoptimised_post_repca", label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

Original UMAP

You can also use the original UMAP projection

DimPlot(tenx.mutant.integrated.sex, label = TRUE, repel = TRUE, label.size = 5, pt.size = 0.5) +
  coord_fixed() +
  theme_void() +
  theme(legend.position = "bottom") +
  guides(colour=guide_legend(nrow=3,byrow=TRUE, override.aes = list(size=4)))

Representation

look at cluster representation

plot

## this function writes the next bit of code for you
ploty <- c()
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
  ploty <- paste0(ploty, "list_UMAPs_by_cluster[[", i, "]]", " + ")
}

## plot
library(gridExtra)
grid.arrange(list_UMAPs_by_cluster[[1]] , list_UMAPs_by_cluster[[2]] , list_UMAPs_by_cluster[[3]] , list_UMAPs_by_cluster[[4]] , list_UMAPs_by_cluster[[5]] , list_UMAPs_by_cluster[[6]] , list_UMAPs_by_cluster[[7]] , list_UMAPs_by_cluster[[8]] , list_UMAPs_by_cluster[[9]] , list_UMAPs_by_cluster[[10]] , list_UMAPs_by_cluster[[11]] , list_UMAPs_by_cluster[[12]] , list_UMAPs_by_cluster[[13]] , list_UMAPs_by_cluster[[14]] , list_UMAPs_by_cluster[[15]] , list_UMAPs_by_cluster[[16]] , list_UMAPs_by_cluster[[17]] , list_UMAPs_by_cluster[[18]] , list_UMAPs_by_cluster[[19]] , list_UMAPs_by_cluster[[20]] , list_UMAPs_by_cluster[[21]] , list_UMAPs_by_cluster[[22]] , list_UMAPs_by_cluster[[23]] , list_UMAPs_by_cluster[[24]] , list_UMAPs_by_cluster[[25]] , list_UMAPs_by_cluster[[26]] , list_UMAPs_by_cluster[[27]] , list_UMAPs_by_cluster[[28]] , list_UMAPs_by_cluster[[29]] , list_UMAPs_by_cluster[[30]] , list_UMAPs_by_cluster[[31]], ncol = 5)

## for loop which takes each cluster and makes a list of cells and then plots a highlighted plot and adds it to a list

## make a df of the number of 
n_per_cluster <- as.data.frame(table(tenx.mutant.integrated.sex@meta.data$seurat_clusters))
rownames(n_per_cluster) <- n_per_cluster$Var1
n_per_cluster <- n_per_cluster[,2]

## make a blank list
list_UMAPs_by_cluster <- vector(mode = "list", length = length(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)))

## for loop
for(i in seq_along(levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters))){
  ## make a list of cells
  list_of_cells <- rownames(tenx.mutant.integrated.sex@meta.data[which(tenx.mutant.integrated.sex@meta.data$seurat_clusters == levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i]), ])
  umap_plot <- DimPlot(tenx.mutant.integrated.sex,  reduction = "umap", label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = list_of_cells) + 
  coord_fixed() + 
  scale_color_manual(values=c("#000000", "#f54e1e")) + 
  theme_void() + 
  labs(title = paste("cluster", levels(tenx.mutant.integrated.sex@meta.data$seurat_clusters)[i], "\n", "(n = ", n_per_cluster[i],")")) + 
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")
  ## add to the list
  list_UMAPs_by_cluster[[i]] <- umap_plot
}

## plot
grid.arrange(list_UMAPs_by_cluster[[1]] , list_UMAPs_by_cluster[[2]] , list_UMAPs_by_cluster[[3]] , list_UMAPs_by_cluster[[4]] , list_UMAPs_by_cluster[[5]] , list_UMAPs_by_cluster[[6]] , list_UMAPs_by_cluster[[7]] , list_UMAPs_by_cluster[[8]] , list_UMAPs_by_cluster[[9]] , list_UMAPs_by_cluster[[10]] , list_UMAPs_by_cluster[[11]] , list_UMAPs_by_cluster[[12]] , list_UMAPs_by_cluster[[13]] , list_UMAPs_by_cluster[[14]] , list_UMAPs_by_cluster[[15]] , list_UMAPs_by_cluster[[16]] , list_UMAPs_by_cluster[[17]] , list_UMAPs_by_cluster[[18]] , list_UMAPs_by_cluster[[19]] , list_UMAPs_by_cluster[[20]] , list_UMAPs_by_cluster[[21]] , list_UMAPs_by_cluster[[22]] , list_UMAPs_by_cluster[[23]] , list_UMAPs_by_cluster[[24]] , list_UMAPs_by_cluster[[25]] , list_UMAPs_by_cluster[[26]] , list_UMAPs_by_cluster[[27]] , list_UMAPs_by_cluster[[28]] , list_UMAPs_by_cluster[[29]] , list_UMAPs_by_cluster[[30]] , list_UMAPs_by_cluster[[31]], ncol = 5)

Show correspondance with old clusters (Alluvium plot, Sankey diagram)

##This is the set up for this:
## two clusters that differ
table(tenx.mutant.integrated.sex@meta.data$seurat_clusters, tenx.mutant.integrated.sex@meta.data$pre_sex_clusters)

## hemberg uses gvisSankey in https://github.com/hemberg-lab/scmap/blob/3aa2bb487a80a946469393857cea6a6effc618fb/R/Utils.R code - so maybe update with this?

## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)

df_alluvial <- melt(table(data.frame(full_clusters = df_meta_data$pre_sex_clusters, sex_clusters = df_meta_data$seurat_clusters)))

## load required package
#library(ggalluvial)

## plot
ggplot(df_alluvial, aes(y = value, axis1 = full_clusters, axis2 = sex_clusters)) +
  geom_alluvium(aes(fill = sex_clusters),
                width = 0, knot.pos = 0, reverse = FALSE) +
  guides(fill = FALSE) +
  geom_stratum(width = 1/8, reverse = FALSE) +
  geom_text(stat = "stratum", infer.label = TRUE, reverse = FALSE) +
  scale_x_continuous(breaks = 1:2, labels = c("original cluster", "Sex Cluster")) +
  coord_flip() +
  ggtitle("Cluster Identiity in full dataset vs. sex only") +
    theme_classic()

Show representation of genotypes per cluster

prep for dotplot

## make a dataframe that is the meta data
df_meta_data <- as.data.frame(tenx.mutant.integrated.sex@meta.data)

## define order for plotting 
my_levels_sex <- c("0", "9", "28", "2", "29", "16", "22", "23", "15", "21", "30", "4", "3", "18", "7", "8", "6", "20", "12", "26", "13", "5", "11", "25", "1", "17", "10", "24", "14", "27", "19")

## redefine order of clusters:
df_meta_data$seurat_clusters <- factor(x = df_meta_data$seurat_clusters, levels = my_levels_sex)

## make a new df of CLUSTER and IDENTITY
dot_plot_df <- as.data.frame.matrix(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined))
dot_plot_df$cluster <- rownames(dot_plot_df)

## calculate percentage of cells for each genotype
dot_plot_df_pc <- (as.data.frame.matrix(prop.table(table(df_meta_data$seurat_clusters, df_meta_data$identity_combined), margin = 2)) * 100)

## make a column for cluster names
dot_plot_df_pc$cluster <- rownames(dot_plot_df_pc)

## melt dataframe for plotting
library(reshape2)
dot_plot_df_pc_melted <- melt(dot_plot_df_pc, variable.name = "cluster")
colnames(dot_plot_df_pc_melted)[2] <- "identity"

## melt the raw number too
dot_plot_df_melted <- melt(dot_plot_df, variable.name = "cluster")
colnames(dot_plot_df_melted)[2] <- "identity"
colnames(dot_plot_df_melted)[3] <- "raw_number"

## merge together
identical(dot_plot_df_melted$cluster, dot_plot_df_pc_melted$cluster)
dot_plot_merged <- cbind(dot_plot_df_melted, dot_plot_df_pc_melted)
dot_plot_merged <- dot_plot_merged[,c(1,2,3,6)]

## redefine order of clusters
dot_plot_merged$cluster <- factor(x = dot_plot_merged$cluster, levels = my_levels_sex)

## where values are zero, add NA
## find wells where it's zero
zero_values <- dot_plot_merged$value == 0
dot_plot_merged$value[zero_values] <- NA

## also do for raw number
zero_values <- dot_plot_merged$raw_number == 0
dot_plot_merged$raw_number[zero_values] <- NA

## reorder x axis:
my_levels_genotype <- c("GCSKO-oom", "GCSKO-29", "GCSKO-2", "GCSKO-19", "GCSKO-3", "GCSKO-21", "GCSKO-13", "GCSKO-28", "GCSKO-10_820", "GCSKO-17", "GCSKO-20", "WT", "WT_10X")

dot_plot_merged$identity <- factor(x = dot_plot_merged$identity, levels = my_levels_genotype)

plot

## plot
dot_plot_identity <- ggplot(dot_plot_merged, aes(y = factor(cluster), x = factor(identity))) +
      ## make into a dot plot
      geom_point(aes(colour=value, size=raw_number)) + 
      scale_color_gradient(low="blue", high="red", limits=c( 0, max(dot_plot_df_pc_melted$value)), na.value="white") +
      #change the colours
      scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white") +
      theme_classic() +
      theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), text=element_text(size=16,  family="Arial")) +
      ylab("Cluster") +
      xlab("Identity") +
      theme(axis.text.x=element_text(size=12, angle=45, hjust=1, vjust=1), axis.text.y=element_text(size=12,), legend.position = "bottom", plot.margin = unit(c(1,3,1,3), "lines")) +
    ## annotate males
    geom_hline(aes(yintercept = 5.5)) +
    ## annotate females
    geom_hline(aes(yintercept = 20.5)) +
    ## annotate pheno 1
    geom_vline(aes(xintercept = 4.5)) +
    ## annotate pheno 2
    geom_vline(aes(xintercept = 5.5)) +    
    ## annotate pheno 3
    geom_vline(aes(xintercept = 7.5)) +
    ## annotate pheno 4
    geom_vline(aes(xintercept = 11.5))

## print
print(dot_plot_identity)

Save and Export

save environment

## This saves everything in the global environment for easy recall later
#save.image(file = "GCSKO_Sex_Branch_Analysis.RData")
#load(file = "GCSKO_Sex_Branch_Analysis.RData")

save modules

#gene_module_df_sex
write.csv(gene_module_df_sex, file = "../data_to_export/gene_module_df_sex.csv")
#save(pb_30k_sex_filtered, pb_sex_filtered, file = "Part_2_input.Rdata")

Save object(s)

## save integrated object to file
#saveRDS(tenx.mutant.integrated.sex, file = "../data_to_export/tenx.mutant.integrated.sex.processed.RDS") 
## restore the object
#tenx.mutant.integrated <- readRDS("../data_to_export/tenx.mutant.integrated.sex.processed.RDS")

Appendix

Useful info

## The tree for monocle is located here:
# monocle.object@principal_graph_aux[["UMAP"]]$dp_mst 
## see more info below in getAnywhere(learn_graph)
## in order to get a plottable dataframe of this:
#test <- as.data.frame(t(as.data.frame(monocle.object@principal_graph_aux[["UMAP"]]$dp_mst)))
#ggplot(test, aes(x = DIMUMAP_1, y = DIMUMAP_2)) + geom_point()

## the curves for slingshot are located here:
#as.data.frame(crv1@curves$curve1$s)
## so a plot can be made by:
#ggplot(as.data.frame(crv1@curves$curve3$s), aes(x= DIMUMAP_1, y = DIMUMAP_2)) + geom_line()

Functions Info

Seurat:::DoHeatmap

monocle:::plot_pseudotime_heatmap

## to understand how the line is stored
getAnywhere(learn_graph)

getAnywhere(aggregate_gene_expression)

so essentially it first takes the counts matrix and subsets it by your gene groups, then it sums the values - if you specify scale then it will calculate the z score of the transformed dataframe

A. Diffusion Map

construct map

## construct diffusion map
## http://www.bioconductor.org/packages/devel/bioc/vignettes/slingshot/inst/doc/vignette.html
## input is a transformed expression matrix (genes as cols and cells as rows)
dm <- DiffusionMap(t(as.data.frame(tenx.mutant.integrated.sex@assays$integrated@data)))

## extract meta data for plotting
df_meta_data <- (as.data.frame(tenx.mutant.integrated.sex@meta.data))

## make combined dataframe
rd2 <- as.data.frame(cbind(DC1 = dm$DC1, DC2 = dm$DC2, identity = as.factor(as.character(tenx.mutant.integrated.sex@meta.data$post_integration_clusters))))

## plot
ggplot(rd2, aes(x = DC1, y = DC2, colour = as.character(identity))) + geom_point(size = 1) + 
  theme(axis.ticks.y = element_blank()) + 
  theme_classic()

## make a density plot for real time correlations using kasia data
## make an annotation dataframe
anno_real_time <- data.frame(monocle.object@colData$cluster_colours_figure, monocle.object@colData$pt, monocle.object@colData$Prediction.Spearman._Kasia, row.names = rownames(monocle.object@colData))
names(anno_real_time) <- c("sex", "Pseudotime", "real_time")

## change the order of the cols (cells) in data frame
col.order <- rownames(anno_real_time[with(anno_real_time, order(sex, Pseudotime)), ])
anno_real_time <- anno_real_time[col.order,]

## add an "order" col to the dataframe to assist in plotting
anno_real_time$order <- c(1:nrow(anno_real_time))

#### plot density plot x
dens1 <- ggplot(anno_real_time, aes(x = order, fill = as.factor(real_time))) + 
                geom_density(alpha = 0.2) +
                #theme_void() + 
                #theme(legend.position = "none")
                ## add annotations for sex
      geom_rect(data = cluster_anno, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2, fill=Median_Pseudotime_of_Cluster), inherit.aes = FALSE) +
    scale_fill_viridis_c(option = "plasma") +
  ## flip coordinates 
  ## geoms below will use another color scale
    new_scale_fill() +
    geom_rect(data = cluster_anno, mapping=aes(xmin=Bx1, xmax=Bx2, ymin=By1, ymax=By2, fill=Identity), inherit.aes = FALSE) +
    scale_fill_manual(values = c("Male" ="#016c00", "Female"="#a52b1e", "Asexual"= "#0052c5", "Committed" = "#f2eb23"))

dens1

TO PUT BACK IN

A.

Put this in after you have decided what is actually male and female, also add a heatmap version below and figure out expression values (e.g. it’s a Z-score at the moment)

Pheatmap version of heatmap old cold to make main heatmap (NOT RUN)

Make annotations to add to the heatmap

## change names for row names to include "module " at the begining of them
row.names(agg_mat) <- stringr::str_c("Module ", row.names(agg_mat))

## add number of cells to the rownames for the module
for(i in seq_along(genes_per_module$Freq)){
  row.names(agg_mat)[i] <- stringr::str_c(row.names(agg_mat)[i]," (n = " ,genes_per_module$Freq[i], ")")
}

## create annotation of clusters for pheatmap:
cluster_anno <- data.frame(cluster = unique(colData(monocle.object)$seurat_clusters))
row.names(cluster_anno) <- cluster_anno$cluster

## clusters were defined earlier as:
male_clusters <- c("13", "5", "11", "25", "1", "17", "10", "24", "14", "27", "19")
female_clusters <- c("16", "22", "23", "15", "21", "30", "4", "3", "18", "7", "8", "6", "20", "12", "26")
asex_clusters <- c("0", "9", "28", "2", "29")

## add identities to the column
cluster_anno$group <- NA
cluster_anno$group[which(cluster_anno$cluster %in% asex_clusters)] <- "Asexual"
cluster_anno$group[which(cluster_anno$cluster %in% male_clusters)] <- "Male"
cluster_anno$group[which(cluster_anno$cluster %in% female_clusters)] <- "Female"
cluster_anno <- cluster_anno[ , 2, drop = FALSE]
cluster_anno

## add median pseudotime per cluster
## help here:
## https://stackoverflow.com/questions/54360855/calculate-mean-for-column-grouped-by-values-of-two-other-columns
## make subsetted dataframe
df_median_pt <- meta_data_df[ ,c("pt", "seurat_clusters")]
## apply across dataframe to get median
mean.df1 <- tapply(df_median_pt$pt, list(df_median_pt$seurat_clusters), median)
mean.df2 <- as.data.frame(as.table(mean.df1))
names(mean.df2) <- c("seurat_clusters", "pt_Median")
rownames(mean.df2) <- mean.df2$seurat_clusters
## to make each value have the mean in the OG dataframe
#merge(df_median_pt, mean.df2)
## add to annotation dataframe
cluster_anno <- merge(cluster_anno, mean.df2, by=0)

## add rownames to dataframe
rownames(cluster_anno) <- cluster_anno$Row.names
## subset to have only info of interest
cluster_anno <- cluster_anno[,c(2,4)]
names(cluster_anno) <- c("Identity", "Median_Pseudotime_of_Cluster")

WT cells by modules over pt (central panel)

## make annotation colours
annotation_colours <- list(Identity = c(Male="#016c00", Female="#a52b1e", Asexual= "#0052c5"),
                           Median_Pseudotime_of_Cluster = magma(12, direction = 1))

## reorder the levels
## make df of data
agg_mat_df <- as.data.frame(agg_mat)
## remove levels in my_levels that are not present here - i.e. clusters that are missing because they are not represented in the 10X data
my_levels_10x_data <- my_levels_sex[which(my_levels_sex %in% colnames(agg_mat_df))]
## sort the values
agg_mat_df <- agg_mat_df[ ,(match(my_levels_10x_data, colnames(agg_mat_df)))]

## order 
#cluster_anno <- cluster_anno[(match(my_levels_10x_data, rownames(cluster_anno))), ]

## reorder columns 
## first, order the annotation
cluster_anno <- cluster_anno[with(cluster_anno, order(Identity, Median_Pseudotime_of_Cluster)),]

## remove the NAs from this
cluster_anno <- cluster_anno[complete.cases(cluster_anno),]

agg_mat_df <- agg_mat_df[ ,match(rownames(cluster_anno), colnames(agg_mat_df))]

## plot heatmap
pheatmap::pheatmap(agg_mat_df, 
                   scale="row",
                   cluster_cols = FALSE,
                   clustering_method="ward.D2", 
                   annotation_col = cluster_anno, 
                   annotation_colors = annotation_colours, 
                   cutree_rows = 12)

#, gaps_col = c(28,29,37)

#row.names(agg_mat) <- factor(row.names(agg_mat), levels = row.names(agg_mat)[module_dendro$order])

## plot heatmap
#pheatmap::pheatmap(agg_mat_df, 
  #                  scale="column",
  #                  cluster_cols = TRUE,
  #                  cluster_rows = module_dendro,
  #                  #clustering_method="ward.D2",
  #                  cutree_rows = 5,
  #                  annotation_col = cluster_anno, 
  #                  annotation_colors = annotation_colours) + 
  # theme(legend.position = "bottom")

Write to excel - old

## old way of writing to an excel
# library(xlsx)
## to increase memory
# #options(java.parameters = "- Xmx1024m"
# write.xlsx(list_of_de_results$md3, file="../data_to_export/DE_analysis.xlsx", sheetName="md3", row.names=FALSE)
# write.xlsx(list_of_de_results$md4, file="../data_to_export/DE_analysis.xlsx", sheetName="md4", append=TRUE, row.names=FALSE)
# write.xlsx(list_of_de_results$md5, file="../data_to_export/DE_analysis.xlsx", sheetName="md5", append=TRUE, row.names=FALSE)
# write.xlsx(list_of_de_results$gd1, file="../data_to_export/DE_analysis.xlsx", sheetName="gd1", append=TRUE, row.names=FALSE)
# write.xlsx(list_of_de_results$fd1, file="../data_to_export/DE_analysis.xlsx", sheetName="fd1", append=TRUE, row.names=FALSE)
# write.xlsx(list_of_de_results$fd2, file="../data_to_export/DE_analysis.xlsx", sheetName="fd2", append=TRUE, row.names=FALSE)
# write.xlsx(list_of_de_results$fd3, file="../data_to_export/DE_analysis.xlsx", sheetName="fd3", append=TRUE, row.names=FALSE)
# write.xlsx(list_of_de_results$fd4, file="../data_to_export/DE_analysis.xlsx", sheetName="fd4", append=TRUE, row.names=FALSE)
# 
# write.xlsx(md3_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="md3", row.names=FALSE)
# write.xlsx(md4_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="md4", append=TRUE, row.names=FALSE)
# write.xlsx(md5_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="md5", append=TRUE, row.names=FALSE)
# write.xlsx(gd1_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="gd1", append=TRUE, row.names=FALSE)
# write.xlsx(fd1_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="fd1", append=TRUE, row.names=FALSE)
# write.xlsx(fd2_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="fd2", append=TRUE, row.names=FALSE)
# write.xlsx(fd3_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="fd3", append=TRUE, row.names=FALSE)
# write.xlsx(fd4_markers, file="../data_to_export/DE_analysis.xlsx", sheetName="fd4", append=TRUE, row.names=FALSE)